WO2001001075A2 - Moteur photogrammetrique servant a la construction de modeles - Google Patents

Moteur photogrammetrique servant a la construction de modeles Download PDF

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Publication number
WO2001001075A2
WO2001001075A2 PCT/US2000/017339 US0017339W WO0101075A2 WO 2001001075 A2 WO2001001075 A2 WO 2001001075A2 US 0017339 W US0017339 W US 0017339W WO 0101075 A2 WO0101075 A2 WO 0101075A2
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Prior art keywords
image
product
space
representation
scene
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PCT/US2000/017339
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English (en)
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WO2001001075A9 (fr
WO2001001075A3 (fr
Inventor
Carl P. Korobkin
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Bethere
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Priority claimed from US09/344,814 external-priority patent/US6912293B1/en
Application filed by Bethere filed Critical Bethere
Priority to AU56351/00A priority Critical patent/AU5635100A/en
Publication of WO2001001075A2 publication Critical patent/WO2001001075A2/fr
Publication of WO2001001075A3 publication Critical patent/WO2001001075A3/fr
Publication of WO2001001075A9 publication Critical patent/WO2001001075A9/fr

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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T17/00—Three dimensional [3D] modelling, e.g. data description of 3D objects
    • G06T17/10—Constructive solid geometry [CSG] using solid primitives, e.g. cylinders, cubes
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T7/00—Image analysis
    • G06T7/70—Determining position or orientation of objects or cameras
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2200/00—Indexing scheme for image data processing or generation, in general
    • G06T2200/16—Indexing scheme for image data processing or generation, in general involving adaptation to the client's capabilities
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
    • G06T2207/00—Indexing scheme for image analysis or image enhancement
    • G06T2207/30—Subject of image; Context of image processing
    • G06T2207/30244—Camera pose

Definitions

  • a parametric solid modeler is an approach to creating geometric models of 3D objects and scenes.
  • Constructive Solid Geometry (CSG) and Boundary Representation (Brep) methods are two fundamental solid modeling methods.
  • CSG uses solid primitive shapes (cones, cylinders, torus, spheres, rectangular prisms, etc.) and boolean operations (unions, subtractions, intersections) to create a solid model.
  • a cylinder subtracted from a cube produces a hole, for instance.
  • Brep methods start with one or more wireframe profiles, and create a solid model by extruding, sweeping, revolving or skinning these profiles.
  • the boolean operations can also be used on the profiles themselves and the solids generated from these profiles.
  • Solids are also created by combining surfaces through a sewing operation. Most commercial solid modeling systems are hybrids combining both CSG and Brep methods to create the desired models. In a parametric model, each geometric entity has parameters associated with it. These parameters control the various geometric properties of the entities, such as the length, width and height of a rectangular prism, or the radius of a fillet. They also control the locations of these entities within the model. Parameters are changed by the user to create a desired model.
  • the task is to construct a 3D model of a scene or object depicted in a sketch or a photograph
  • the use of a conventional solid modeling system can be an arduous task. This is especially true when producing a texture-mapped model, where the original photograph or other imagery is applied to the derived geometric model.
  • the current process typically involves first creating a 3D geometric model then tediously "cutting and pasting" textures onto the model to add realism. Ultimately, the results are limited by the hand-eye coordination of the user. In short, the current approach and available tools cannot achieve desired levels of geometric accuracy, realism (visual fidelitv), are too time-consuming, and require too much skill.
  • One application of the graphics system described herein is for visualizing the placement of one or more physical objects in a physical scene.
  • digital images of those one or more physical objects and a digital image of the physical scene are input to the graphics system and the graphic system maps the objects in those images to a three-dimensional geometric model using cues in the digital image or provided as additional inputs.
  • the graphics system then generates an image output that is an image of the geometric model, thereby showing a virtual image of the physical objects in the physical scene without actually moving those physical objects into the physical scene.
  • One use of the graphics system is for electronic commerce. For example, a consumer captures a digital image of a room in the user's home and provides it to the graphics system. A merchant captures digital images of products it has for sale.
  • the graphics system can combine the images to show the consumer an image of what the product might look like within the room where the user might place the products, if purchased.
  • the user can capture multiple images with some overlap and the graphics system will match them up to form a "mosaic" image that can then be mapped to a geometric model.
  • the image provider might include cues to assist the graphics system in mapping the two-dimensional object onto the three-dimensional model.
  • the user might have an object in the scene that is a rectangular prism (such as a box, dresser, refrigerator, etc) and indicate the bounds of the rectangular prism on the two-dimensional image.
  • the graphics system can handle shapes besides rectangular prisms, such as spheres, flat rectangles, arches, boolean combinations of simple shapes, etc., limited only by the shapes represented in a shape library that forms part of the graphics system.
  • the user selects a shape while viewing an image and then moves the shape around to approximately coincide with an object in the image.
  • the images of the scene and the objects to be inserted do not need to be taken from the same camera position.
  • a consumer might capture an image of a room from an angle, standing at the edge of the room, but a merchant might have captured an image of a rug they are offering for sale with their camera directly over the rug.
  • camera parameters such as camera position in the geometric model, camera rotation, focal length, center of view, etc. Those parameters might already be available if the image capturing device is sophisticated or the camera is always held in a known position, but such parameters are usually not available and the graphics system will generate them from the images and constraints.
  • the graphics system could be a localized system or a client-server system.
  • a server connected to clients over the Internet might be used to create an electronic commerce system in connection with one or more Web sites.
  • Merchants could provide catalogs of goods to the server and the consumer could provide an image of a scene to the server.
  • the server could then return to the consumer an image of how the product would look in the scene. Since the images are mapped into a three-dimensional geometric model, the combination of the objects and the scene appear more realistically combined, even when taken with different camera parameters, than if the image of the object were just distorted and overlaid into an area of the image of the scene.
  • complex interactive objects can be provided for combination with a scene.
  • An object such as a chest of drawers might include the ability to open a drawer and view the effect. Additional, non- image information, such as a product description, pricing, etc., might also be included with a complex object.
  • FIG. 1 is a high-level block diagram of a media processing engine residing on a network in a client-server configuration.
  • Fig. 2 is a detailed block diagram of the media processing engine with inputs and outputs to a network or otherwise.
  • Fig. 3(a) is a wedge structure: Fig. 3(b) is a box structure; Fig. 3(c) is another box structure.
  • Fig. 4(a) shows a cylinder structure
  • Fig. 4(b) shows a sphere structure
  • Fig. 4(c) shows a two box structures constrained to each other by equating point elements
  • Fig. 5(a) shows examples of boolean union, merge, intersection, and difference
  • Fig. 5(b) shows several three dimensional geometric structures and associated geometric elements and two dimensional features corresponding to a projection of the 3D elements into 2D.
  • Fig. 6(a) is an image of an object
  • Fig. 6(b) is the image of the object with annotations added to identify individual edge and point features of the object shown in the image.
  • Fig. 7 is the same image of the object with annotations added to identify connected structure edge and point features of the object shown in the image.
  • Fig. 8(a) shows a 3D construction with the image of Fig. 6(a) with an unsolved box structure in its default initial position and an unsolved camera model
  • Fig. 8(b) shows a scene graph containing the box structure
  • Fig. 8(c) is the scene graph with a sphere structure added
  • Fig. 8(d) is the same scene graph with yet another structure added.
  • Fig. 9(a) is a 3D object construction containing two box structure and one sphere structure and corresponding to the scene graph of Fig. 8(d);
  • Fig. 9(b) is the scene graph of Fig. 8(d) with an additional cylinder structure added.
  • Fig. 10(a) shows the relationship between a line in 3-space, a camera system in the same space, and the projection of that line into the imaging plane of the camera;
  • Fig. 10(a) shows the construction of an error function quantifying the error between a line segment annotated into the imaging plane of the camera and the projection of the 3D line through the camera and into the imaging plane
  • Fig. 11(a) shows another view of the 3D construction of Fig. 8(a).
  • Fig. 11(b) shows the back projection of the 3D box into the image for the construction of Fig. 8(a) and unsolved parameters;
  • Fig. 11(c) shows a view of the construction of Fig. 8(a) with camera and box parameters recovered;
  • Fig. 1 1(d) shows the back projection of the 3D box into the image for the construction of Fig. 8(a) and solved camera and geometry parameters.
  • Fig. 12(a) shows a sphere structure constrained to the top of a box structure pertaining to the construction of Fig. 9(a);
  • Fig. 12(b) shows the same construction with a boolean merge operation applied between the box and sphere structures;
  • Fig. 12(c) shows construction of Fig. 12(b) with a box structure added;
  • Fig. 12(d) shows the construction of Fig. 12(c) with a boolean difference operation applied between the two box structures;
  • Fig. 12(e) shows the construction of Fig. 12(d) with a cylinder structure added;
  • Fig. 12(f) shows the construction of Fig. 12(e) with a boolean merge applied between the cylinder structure and the second box structure.
  • Fig. 13(a) shows a rectangular straight pipe extruded solid from a rectangle extrusion profile and linear extrusion path;
  • Fig. 13(b) shows a curved profile extruded
  • FIG. 13(c) shows a revolved solid generated from a curved profile and revolution axis.
  • Fig. 14 is a flow diagram showing the process for constructing 3D objects as assemblages of structures.
  • Fig. 15 is a flow diagram showing visibility processing on geometric models to determine which sub facets of the model each associated camera sees.
  • Fig. 16 is a flow diagram showing how visibility between cameras and geometry is resolved to reapply imagery back onto geometry.
  • Fig. 17(a) is a diagram of a Phantom Cursor graphical user interface mechanism
  • Fig. 17(b) is a diagram of the default 3-space construction created upon initialization of a phantom cursor.
  • Fig. 18(a) is an image of a room scene
  • Fig. 18(b) is the image of the room scene with a default quadrilateral phantom cursor graphic in its initial "floating" position
  • Fig. 18(c) shows the image of the room scene with the phantom cursor graphic modified by the user
  • Fig. 18(d) shows the phantom cursor image annotation overlay from Fig.
  • Fig. 18(e) shows the 3-space construction for the current phantom cursor example.
  • Fig. 19 is a flow diagram outlining the phantom cursor process.
  • Fig. 20 is a flow diagram outlining an image mosaic process leveraging the phantom cursor process
  • Fig. 21 shows three images of a room scene used as example input to the system mosaic processing method
  • Fig. 22 shows the three images with annotations applied as part of the mosaic processing
  • Fig. 23 shows the 3-space construction corresponding to the mosaic processing of the images of Fig. 22.
  • Fig. 24(a) shows a detailed diagram of a quadrilateral phantom cursor structure
  • Fig. 24(b) gives the parameterization of the vertices of a quadrilateral phantom cursor
  • Fig. 24(c) is a correspondence table that maps the relationship between 2D edge features and 3D elements of a quad phantom cursor.
  • Fig. 25 is an image created by mosaic of the three images of Fig. 21.
  • Fig. 26 is a flow diagram of an image collage process that leverages the phantom cursor process.
  • Fig. 27 is an image of a room scene with annotations added for collage processing.
  • Fig. 28(a) is an image of a rug product to be used for the collage processing:
  • Fig. 28(b) is an image of a picture product to be used for the collage processing.
  • Fig. 29(a) shows the 3-space construction corresponding to the collage processing of the image of Fig. 27;
  • Fig. 29(b) is the scene graph corresponding to the collage construction of Fig. 29(a).
  • Fig. 30 is an image that is the collage composition of the image of Fig. 27 with the product images of Fig. 28(a) and Fig. 28(b).
  • Fig. 31 shows an image of a room scene and an image of a television object to be composed with the scene image through collage processing.
  • Fig. 32 shows an image of the room scene after composition with the image of the television, as well as a generated alpha image that will correct undesired hidden surface regions.
  • Fig. 33 shows a final image of the collage construction of Fig. 32 after application of the alpha image.
  • Fig. 34 is a flow diagram outlining the process of creating a 3D scene model from one or more images and levering the phantom cursor.
  • Fig. 35 is an image of a room interior with annotations added for construction of a 3D model of the room based on the process outlined in Fig. 34.
  • Fig. 36 is a 3D model of the room depicted in Fig. 35.
  • Fig. 37 is a scene graph corresponding to the 3D model of Fig. 36.
  • Fig. 38 is an example of a 3D solid model constructed with functional parametric components.
  • Fig. 39 is a flow diagram showing how to integrate constructed object and scene models with various interaction and scaling options.
  • Fig. 40(a) is an image of a room scene with an annotation added to the depicted floor" representing the anchor point for an object to be inserted;
  • Fig. 40(b) is the image with the rug object of Fig. 28(a) inserted at the anchor point on the floor.
  • Fig. 41(a) is the image with the rug object moved along the floor, away from the initial from the original anchor point;
  • Fig. 41(b) is the same image with the rug moved to some other location on the floor.
  • Fig. 42(a) shows the scene graph of the scene construction for the scene of image of Fig. 40(a) prior to insertion of the rug object
  • Fig. 42(b) shows the construction scene graph after the insertion of the rug object
  • Fig. 42(c) shows the constructed scene model with the inserted product model.
  • Fig. 43 shows the 3-space construction for a "force-fit" model integration procedure.
  • Fig. 44(a) is an image of a brown wooden CD cabinet;
  • Fig. 44(b) is an image of a cubical pine storage box.
  • Fig. 45(a) is an image of a dark brown wood cabinet
  • Fig. 45(b) is an image of a cubical pine storage box.
  • Fig. 46 shows four images of a room scene generated by the construction of a 3D model of the room in Fig. 35, insertion of constructed 3D models of the products depicted in Fig. 44 and Fig. 45, and user-interactive navigation of the scene and movement of the product models within the scene.
  • Fig. 47 shows a 3D graphical product information display that "pops-up" provide information about a product.
  • Fig. 48 shows the room scene of Fig. 46 with both 2D and 3D product information displays active.
  • Fig. 49 is a diagram of multiple instances of the media processing engine operating over a network in client-server configuration.
  • Fig. 50 is a diagram of multiple instances of the media processing engine operating over a network in client-server configuration and operating as an electronic commerce merchandising system.
  • a photogrammetric processing engine placed between the user and the solid modeling engine transforms the conventional "hand-eye" coordinated modeling interface to one driven by user-interaction with the input photographs.
  • Photo grammetry is the art, science, and technology of obtaining reliable information about physical objects and the environment through processes of recording, measuring, and interpreting photographic images and patterns of electromagnetic radiant energy and other phenomena.
  • the user paradigm becomes one of molding and sculpting directly from imagery.
  • the system automatically sizes and positions (parameterizes) geometric entities, thus "fitting" 3D model entities to with 2D image features.
  • the system also automatically determines 3D parametric camera models for each photograph of the scene (e.g. location, pose, focal length, center-of-projection). Having derived geometry and camera models, the system automatically produces a visual reconstruction and rendering of the scene through re-projection of the input imagery onto derived geometry.
  • a network-based client-server system leveraging the solid modeling system is disclosed. This system is suited for a wide range of applications, from professional CAD development and collaboration to Internet e-commerce activities.
  • the Internet is rapidly becoming a dominant medium for commerce.
  • One area enjoying rapid growth is that of online shopping.
  • the Internet offers unprecedented convenience to consumers searching and researching products, there exist barriers to consumer willingness or ability to purchase certain products online. High among these barriers is a shopper's desire to physically see or try out a product ahead of time.
  • a consumer In shopping for kitchen cabinets, a consumer is most interested in how they would look in their home, not on a web page.
  • an e-commerce system allows online shoppers to "take home" products from online catalogs and try them out as an integral part of their online shopping experience.
  • a merchant serves a client application enabling end-users to construct 3D representations of their environments from their own digital photographv.
  • a server node serves functional models of products that are readily composed with the end-user environments and allow the end-user to understand how they look, fit. and function.
  • MPE Media Processing Engine
  • Photogrammetric Media Processing Engine (MPE) 11 is shown in Fig. 1. A more detailed diagram is shown as Fig. 2.
  • the main functional components of MPE 11 are Graphical User Interface (GUI) 12, Photogrammetric Modeling Engine (PE) 13, Scene Graph and Ancillary Data Structures (SG) 14, Built-in 3D Parametric Object and Scene Geometry Library (BL) 18, Solids and Surfaces Geometry Engine (GE) 15, Visual Reconstruction Engine (VE) 16, and Rendering Engine (RE) 17.
  • Digital data and program transmission network 10 is a source of data and program input and output of the system and is an integral component and backbone of the client-server data processing model. In its current embodiment, the network is the Internet. However, the system is not limited to any particular physical data transmission network and data transmission protocol. All data input, output, and process control is directed by user-interactive input 25 through Graphical User Interface 12. The disclosed invention system and methods are not dependent on a particular design and implementation of 12.
  • the system imports and exports 2D digital images as a fundamental data type.
  • User digital images 24 are those acquired or obtained by the user and supplied to the system or the network. Examples of user images are those acquired from acquisition devices such as a digital cameras and scanners or transferred from mediums such as CDROM.
  • User digital images 24 are imported into the system or uploaded to network 10.
  • Network digital images 21 are digital images downloaded from network 10. User digital images might also be downloaded as network images 21. As an example, user images are processed by a third and delivered over the Internet.
  • the system imports and exports 3D geometric models as a fundamental data type. Geometric models are typically imported and exported with sets of 2D image texture maps assigned to the models. User 3D geometric models 23 are those acquired or obtained by the user and supplied to the system or the network. Examples of such models are those acquired from acquisition devices such as scanner devices or transferred from mediums such as CDROM. User 3D models are imported into the system or uploaded to network 10. Network 3D models 20 are models downloaded from network 10. Ancillary data input 22 imports general and information from network 10. For example, for images 21 and models 20, ancillary data 22 might specify size, color, price, and availability parameters. The user may also supply ancillary data, such as certain known dimensions of an object or feature, through user interactive input 25.
  • the system imports and exports project databases providing a convenient mechanism for project saving, restoration, and work collaboration.
  • Project databases include system generated scene graph and other data structures and accompanying data.
  • Data 22 imports project databases and all general data associated with incoming 2D and 3D media from network 10.
  • Data input 28 imports project databases from storage medium such as system memory or disk.
  • Output 26 exports project databases to storage mediums such as system memory or disk.
  • Output 27 exports project databases to network 10.
  • System output 26 consists of 2D images, generated 2D images and image compositions, 3D models, 3D models and 3D texture-mapped object and scene compositions generated from 2D images, and compositions of all of these. Output 26 is directly viewed on a computer display device, stored on digital storage mediums such as computer hard disk. System output 27 sends system output 26 to network 10.
  • Scene graphs are used to store geometric, topological, and visual construction information.
  • a scene graph conveniently and efficiently encodes a hierarchical organization of the components that comprise the scene.
  • the scene graph and ancillary data structures are stored in data structures unit 14.
  • a scene graph is made up of nodes that represent geometric components drawn, properties of the components, hierarchical groupings of nodes, and other information such as cameras and visual reconstruction information.
  • Nodes are connected by links in a parent-child hierarchy.
  • the scene graph is processed or traversed from node to node by following a prescribed path of links.
  • Nodes and links contain parameters.
  • Nodes contain the parameters associated with the geometric structures they specify.
  • Links contain the spatial relationships (e.g. rotation, translation, scale) between the geometric primitives (structures) of the interconnected nodes.
  • a tree structure is an example of one of the most common types of graph structures.
  • Another form of scene graph is a directed acyclic graph (DAG). The specific form of the scene graph is not essential to the invention as described here.
  • photogrammetric modeling engine 13 and visual reconstruction engine 16 read from and write to the scene graph data structure.
  • rendering engine 17 reads the scene graph data structure and composes a visual representation.
  • the Photogrammetric Modeling Engine 13 is highly-automated photogrammetry and constraints based modeling system that embodies a process for recovering parametric 3D geometric structure and camera models from 2D images. Under the direction of user interactive input 25. the system produces parametric and non- parametric polyhedral and solid (volumetric) models of image compositions and 3D models of objects and scenes depicted in supplied 2D digital imagery. Modeling Engine 13 recovers parametric camera models for each input image of a construction project.
  • Geometric constructions generated by the system are hierarchical and non- hierarchical assemblages of geometric components called "structures". Structures are parameterized geometric primitives, such as points, lines, line segments, planes, cubes, squares, rectangles, boxes, cylinders, cones, frustums, wedges, surfaces of revolution, and extrusions.
  • a structure contains a set of parameters describing its size and shape.
  • the coordinates of each vertex of a structure are expressed as a linear combination of these parameters, relative to the structure's internal coordinate frame.
  • the spatial extent of a structure is specified as a bounding volume.
  • half-wedge structure 49 is defined by shape parameters wed_width, wed_height, and wed_depth.
  • a vertex VE1 is expressed as ( -wed width, wed_depth, 0 ).
  • the spatial extent of structure 49 is defined by rectangular bounding volume 54.
  • cylinder structure 50 is described by three parameters cyl-radiusA, cyl-radiusA, and cyljength.
  • the first two parameters control diameter and the third controls length.
  • the cylinder is circular; otherwise the cylinder is elliptical.
  • the extent of the cylinder is given by bounding box 58.
  • the cylinder might also contain radius parameters at the "other end" of the cylinder, thus defining tapered cylinder profiles.
  • sphere structure 51 is described by radius parameter sphere-radius and bounding box 59. Structures are also generated, as in the case of extruded and revolved solids. Shown in Fig. 13(a) is an extruded rectangular box structure XX. It is generated by translating rectangular profile element XI 0 along linear extrusion path element P10. Fig. 13(b) shows an extruded object XX with a more complex curved profile element X20
  • Fig. 13(c) shows a solid structure XX generated by revolving profile element X30 around sweep axis element P30.
  • the system embeds a library of built-in structures 18. including, but not limited to, fundamental geometric structures typically associated with conventional solid modeling systems.
  • Library 18 is fully extensible and the number and types of structures supported may be tailored to suit the application software used. New classes of structures are pre-programmed into built-in library 18, imported as user input 23, or downloaded as network input 20.
  • Geometric structures are comprised of elements.
  • An element is a component of a structure or higher-level construct or possibly a 3D structure itself, as in the case of a point. It represents a 3D structure part or shape that can be transformed and projected into the planes of related 2D input images. Examples of elements include points, lines, line segments, planar polygons, circles, and curves.
  • structures 49, 52, and 53 comprise edge and point type elements.
  • Point elements are structure vertices and edge elements are line segments forming edges between structure vertices.
  • "box" structure 52 has eight vertex elements, VE40 through VE47. Connecting these vertex elements are 12 edge elements.
  • edge element EE24 is the line segment formed between vertices VE42 and VE46.
  • the number and type of elements supported by modeling engine 13 is fully extensible. The system does not limit the vocabulary of geometric elements supported. Any component of a geometric structure or higher-level construct that can be projected and identified in the imaging plane of a camera (plane of an image) may be defined as an element.
  • Point 64 is itself a structure and an element.
  • Edge structure 66 is and edge element comprised of two vertex (point) elements and a connecting line segment.
  • Box structure 68 contains individual point elements and edge elements.
  • Cylinder structures 70 and 74, and sphere structure 72 are comprised of point elements, edge segment elements, circle, and ellipse elements.
  • elements include defining profiles and paths.
  • the relationships between component structures in the hierarchy of a model assembly are represented by parametric operators, which encode spatial and geometric relationships between structures.
  • spatial operators are the affine geometric transformations of rotation R, translation T, and scale S. In general, operators may also specify non-affine spatial transformations.
  • Geometric operators determine the topology of the overall model assembly.
  • Fig. 5(a) shows the boolean operators union 60, merge 61, intersection 62, and difference 63.
  • Geometric operators also include, but are not limited to. blending, sweeping, imprinting, covering, lofting, skinning, offsetting, slicing, stitching, sectioning, and fitting.
  • Geometric operators are processed by geometry engine 15.
  • 3D geometric models are related to 2D images through annotations marked on images called features.
  • a feature represents the projection of a 3D model element into the plane of projection of the camera attached to the image.
  • the parameterized camera model attached to the image specifies the projective transformation.
  • Fig. 5(b) illustrates several projective relationships between 3D structure elements and 2D image features.
  • Point, line segment, and circle elements in 3-space project into point, line segment, and ellipse features in 2-space, respectively.
  • 3D point structure 64 projects into 2D point feature 65.
  • 3D edge structure 66 projects into 2D edge feature 67.
  • 3D point and edge segment elements of box structure 68 and cylinder structures 70 and 74 project into point and edge features 69.
  • 3D circle elements of cylinder 70 and ellipsoid elements of cylinder 74 project into 2D ellipse features 71.
  • 3D circle features of sphere 72 project into 2D circle features 73.
  • the modeling engine modeling process entails the marking of features in one or more input images and the establishment of correspondence between these 2D features and geometric elements of a 3D construction.
  • the user places features in images through user-interactive input 25 and interface 12.
  • An optional gradient -based technique aligns marked edges to image pixel features with sub- pixel accuracy.
  • image 1100 of Fig. 6 is annotated with edge and point features, resulting in image 1101.
  • edge features F20 through F31 mark selected edge regions.
  • each 2D edge feature is composed of two endpoint vertices and a connecting line segment.
  • edge feature EF24 contains endpoint feature vertices VF10 and VF11.
  • edge features need not align with depicted endpoints of a selected edge in the image; the placed line segment need only align with the associated edge in the image.
  • point features VF50 and VF60 mark individual point feature positions in the image.
  • Correspondence establishes a relationship between 3D elements of structures in global position and 2D image features. A single correspondence is a feature- element pair. A geometric element can have multiple image correspondences.
  • Correspondence types define valid feature-element pairs that quantify the degree of geometrical coincidence of the projected view of a geometric element and a 2D image feature, relative to the camera associated with the image.
  • Valid correspondence types are defined for each geometric element or higher-level construct.
  • Example feature- element types include point-point, point-edge, and edge-point pairs.
  • the system is fully extensible and not limited to a specific vocabulary of correspondence types.
  • the degree to which an annotated 2D image feature and a projected geometric element align is expressed in terms of a correspondence error. The manner by which the correspondence error is measured depends on the correspondence type.
  • a correspondence error metric is defined for each valid correspondence type and quantifies the degree of geometrical coincidence of the projected view of the element and the feature.
  • Correspondences are established implicitly or explicitly. In the former case, features are placed in images and 3D structure elements are explicitly associated with them. In the latter case, images are annotated with features that directly (implicitly) correspond to structures and their elements. Correspondences may also be implicitly established by application program design.
  • edge correspondences are established between "box" structure 52 of Fig. 3(b) and image 1100 of Fig. 6.
  • edge features EF20, EF21, EF22, EF23, and EF24 are annotated, as shown in image 1101. These features are then explicitly assigned to 3D structure 52 edge elements EE20, EE21, EE22, EE23, and EE24, respectively.
  • feature-to-element pairings are established through a "point-and-click" user interactive interface.
  • image 1101 is annotated with structure 52 by placing 2D vertex features into the image that directly correspond to 3D vertex elements VE40 through VE47. as shown in Fig. 7 image 1103.
  • edge elements adjoining the vertices implicitly correspond to (and serve as) edge features in the image.
  • interconnecting edges "rubber-band" in place, thus presenting the structure as a "pliable" 2D annotation object.
  • Constraints are values set for parameters of a construction. By reducing the number of unknown parameters of a construction, the computational load of parameters to be recovered by modeling unit 13 is reduced. Constraints may be placed on any values of a construction parameter-space, including structure shape parameters, spatial and geometric structure operators, and intrinsic and extrinsic camera parameters. Constraints on the parameter-space of a 3D model and camera construction may be defined by the application or explicitly specified by user-interactive input 25 through 12. Structures may have their individual shape parameters constrained to specific fixed values. For example, setting structure 52 parameters base width, base_depth, and basejteight to x units each establishes a cube structure of 2x units in each dimension.
  • the system treats parameters symbolically, so parameters are conveniently shared between structures.
  • Equality is a common type of constraint. For example, if Fig. 3 structure 53 parameter door_depth is set equal to structure 52 parameter base iepth, the depth of both structures remain the same, even under varying values of basejiepth.
  • Fig. 9(a) structures 51, 52 and 53 are spatially constrained by the rotation R, translation T, and scale S operators linking them.
  • R is set to null (no rotation in any axis) between 51 and 52 and between 52 and 53.
  • translation Jis set such that the midpoints of structures 52 and 53 align in the -axis and z-axis and the minimum bounding extents of structures 52 and 53 are coincident in the v-axis.
  • translation T is set such that the midpoints of structures 52 and 53 align in the x-axis and z-axis.
  • Scale S is set to unity for all structures.
  • a user interactively selects and equates structure vertex elements.
  • structure 53 is attached to the top of structure 52 by the user selecting and equating vertex elements VE44 and VE13, VE47 and VE16, and VE46 and VE15.
  • This constraint specification sets the spatial translation T and rotation R between the structures to zero while also equating their width and depth shape parameters.
  • Camera models are parametric and include variable parameters describing external pose and internal settings.
  • Cx, Cy, and Cz describe 3-space position and three parameters Rx. Ry, and Rz describe angular viewing direction.
  • parameters include focal length/and center-of-projection parameters cop x and cop_y.
  • a camera's pose and internal projection parameterization is composed into a homogenous 4x4 matrix. The composition of the intrinsic projection transformation and extrinsic pose transformations specify the mapping of an arbitrary 3D point in the global 3D coordinate frame into the camera ' s image plane.
  • modeling engine 13 automatically sizes and positions (parameterizes) geometric structures. Modeling engine 13 also determines
  • 3D camera models (location, pose, focal length, center-of-projection). Camera model derivation is an intimate component of the photogrammetric process and is central to subsequent visual scene and object reconstruction.
  • Modeling engine 13 recovers all unknown unconstrained variables of a construction parameter space to form a reconstruction of depicted objects or scenes and the cameras that image them.
  • the solution of the geometry and camera parameterization aligns 3D model elements with corresponding 2D image features and allows recovered camera system to accurately project (re-project) their imagery onto geometric representations.
  • Modeling engine 13 solves for the variable parameters by minimizing the aggregate correspondence error (sum of all correspondence errors). This is a nonlinear multivariate function referred to as the objective function O. Minimizing the objective function requires nonlinear minimization. Defined constraints eliminate variables and thereby reduce the dimensionality of the unconstrained nonlinear minimization process. In addition, it makes use of invariants and linearities to progressively minimize the function.
  • Fig. 10(a) illustrates an edge-to-edge correspondence type and implementation of the present embodiment.
  • Line element E70 of a 3D model projects onto image plane 71 of camera CIO.
  • the 3-space line E70 is defined by a pair of vectors
  • the position of camera CIO with respect to world coordinates is expressed in terms of a rotation matrix Rj and a translation vector Tj.
  • An annotated image edge feature F75 is delimited by image feature vertices VF20 and VF21, with image coordinates (x ⁇ ,y0 and ( ⁇ 2 ,y_2), respectively, and is denoted as ⁇ (x ⁇ ,y ⁇ ),(x 2 ,y2) ⁇ -
  • the disparity (correspondence error) between a computed image edge B74 and an annotated image edge F75 is Errj , for the rth correspondence of the model construction.
  • Fig. 10(b) shows how the error between edges B74 and F75 is calculated.
  • Points on the annotated edge segment F75 are parameterized by a single scalar variable s € [0,l ⁇ where / is the length of the edge.
  • a function, h(s) returns the shortest distance from a point on the segment, P(s), to computed edge segment 74.
  • the reconstruction minimizes the objective function O that sums the disparity between the projected edge elements of the model and the marked edge features in the images.
  • the objective function O ⁇ Err, is the sum of the error terms resulting from each correspondence i. O is minimized using a variant of the Newton-Raphson
  • n method which involves calculating the gradient and Hessian of O with respect to the va ⁇ able parameters of the camera and the model.
  • symbolic expressions for m are constructed in terms of the unknown model parameters.
  • the minimization differentiates these expressions symbolically to evaluate the gradient and Hessian after each iteration.
  • the reconstruction algorithm optimizes over the parameters of the model and the camera positions to make the model conform to the observed edges in the images.
  • Modeling engine 13 w ⁇ tes to, and reads from, scene graph unit 14.
  • This data exchange includes 2D images 30, paramate ⁇ zed geometric structures 31, paramete ⁇ zed operators and constraints 32, paramete ⁇ zed camera models 33, and ancillary data 34 which includes images features and correspondences.
  • Solids and Surfaces Geometry Engine (GE) 15 reads the scene graph and performs specified geomet ⁇ c operations on the construction.
  • the resulting fundamental and complex geomet ⁇ c structures and models are sent back to scene graph unit 14 via 39 or are stored in built-m library 18 via 35 and augment the library. The latter generates structures beyond those pre-existing in 18.
  • the output 40 of geometry engine 15 is also sent to visual reconstruction processor 16.
  • GE 15 supplies operations on and between all structure classes. This includes solid structures operating on surfaces structures and vice versa. Operations on and between geomet ⁇ c structures include, but are not limited to, booleans, extrusions, sweeps, revolutions, lofts, blending, shelling, and local manipulation and deformation.
  • boolean operations between a given set of shapes allow the creation of an infinite number of simple and complex shapes.
  • Fig. 5 shows example boolean operations union 60, merge 61, intersection 62, and difference 63 performed by GE 15.
  • Geometric operators also include, but are not limited to, blending, sweeping, lmp ⁇ ntmg, cove ⁇ ng, lofting, skinning, offsetting, slicing, stitching, sectioning, and fitting.
  • Fig. 13(a) and Fig. 13(b) show examples of extrusion processing by GE 15.
  • Fig. 13(c) shows an example of surface-of-revolution processing by GE 15.
  • the Visual Reconstruction Engine 16 automatically applies 2D input imagery onto 3D geometric models using recovered 3D camera models.
  • Text-mappmg The application of the input imagery onto the output geometry involves a re-sampling of the input imagery, a process commonly called texture-mappmg.
  • Conventional texture-mapping systems typicallv require users to manually and explicitly specify texture-to-geometry mappings. Such conventional approaches are labor-intensive and yield inaccurate results.
  • Visual reconstruction engine 16 automatically determines the texture mapping strategy for visually reconstructing scenes and objects on a facet-by-facet basis. This includes determination of which texture map(s) to apply to each facet of the scene geometry, calculation of the texture coordinate parameterization, and texture rectification (as required). Camera-image assignments are made to constructed geometry based on the results of visibility tests and established image quality criteria.
  • the input to 16 is a database containing 3D object and scene geometry models, camera models 36, imagery 37 associated with each camera model, and geometric object and scene models 38 and 40.
  • Output 42 of 16 is a graphical database that is ready for processing by rendering engine 17 or export for further processing by another graphical database rendering apparatus or storage.
  • the invention is not limited to any particular geometry, camera, or texture data format or rendering procedure.
  • the specific output of reconstruction engine 16 output 42 is a function of the processing modality of media processing engine 11 and the media type produced.
  • visual reconstruction processor 16 For processing of 3D texture-mapped geometric object and scenes from one or more 2D images, visual reconstruction processor 16 produces a graphical database that includes camera maps, texture maps and coordinates, camera models, and 3D object and scene geometry.
  • a camera map is an assignment of cameras to scene geometry. For each triangular facet of model geometry seen by at least one camera, a camera map produced by 16 designates a single "best" camera or group of cameras whose imagery is available and appropriate to texture map the triangle.
  • Hybrid Camera-Geometry Visibility Processing is required to determine, for each (camera, image) pair in a given 3D scene, which facets of scene geometry (in whole or in part), are visible from the viewpoint of the camera.
  • the system For visible/hidden surface determination, the system considers back-facing surfaces, surface visibility with respect to the extent of each camera's viewing frustum, and surface visibility within a camera's view frustum and in consideration of occlusions (partial and full) amongst the various surfaces. The latter two categories are referred to here as window visibility and occlusion visibility, respectively.
  • Window visibility is resolved by intersecting surfaces with a camera's viewing frustum, subdividing surfaces based on intersections, and selecting portions ⁇ contained within the viewing frustum.
  • Occlusion visibility is resolved by computing the intersections of object surfaces with each other. Then for each set of intersections, it is determined which surface is closer to the viewer (camera) and thus visible.
  • Visual reconstruction engine 16 presents a hybrid visibility processing approach, invoking both object-space and image-space visibility computation.
  • Image- space and object-space visibility processing algorithms differ in the precision with which they compute the visible surfaces.
  • Image space algorithms determine visible surfaces by examining surfaces at the pixel level, post-projection in to the imaging plane of the viewer.
  • Object space algorithms directly compare surfaces in the defined space.
  • Object space calculations are employed for computing the intersections of surfaces in the defined global 3D coordinate system of the geometry.
  • Image-space computations process geometry visibility in the projected screen space of each camera.
  • a hybrid approach is preferred, since it can be fully implemented in software or make use of commonly available computer graphics hardware. It allows the system to run on a host computer systems with or without 3D graphics capabilities (software or hardware). For systems with 3D graphics capabilities, including hardware acceleration, the application can effectively leverage available resources.
  • the visual reconstruction process is executed in two stages.
  • the first stage of the visual reconstruction process is a determination of the visible triangles relative to each scene camera. This is a multiple step process carried out for each camera.
  • Fig. 15 shows a flow diagram of the process.
  • step ST70 all triangles are initialized as being visible and surface normals are computed for each.
  • Triangles are backface filtered by taking the dot product between the triangle normal n and the camera viewing direction v determines if the triangle is back or front facing to the camera. If back- facing, a triangle is marked as not visible.
  • each triangle not filtered out at step ST70 is clipped against the camera frustums.
  • An object-space clipping algorithm checks each triangle in three stages: trivial accept, trivial reject, and clip. If a triangle is trivially accepted, then it remains marked visible. If a triangle is trivially rejected (culled out of the cameras view), then it is marked not visible to the camera. Otherwise, the triangle is intersected (clipped) against the camera frustum. The triangle is then triangulated (subdivided) with the new intersection vertices. In the present implementation, Delaunay triangulation is employed. The resulting sub-triangles that are within the view frustum are marked as visible to the camera; those outside the frustum are marked as not visible.
  • occluded triangles surfaces are identified.
  • An example of an occluded triangle is one that is fully or partially obscured from a camera's viewpoint by another triangle or group of triangles. Triangles that are fully occluded are marked as not visible. Triangles that are not occluded at all remain marked visible. Otherwise, for each partially occluded triangle, a list of those triangles that occlude it is produced.
  • the system or the user selects image-space computation or object-space computation for resolution of hidden surfaces. If an object-space computation is selected, process flow proceeds to step ST73. Otherwise, process flow proceeds to step ST74.
  • an object-space algorithm computes analytic visibility in two stages.
  • the first stage is a sort of the triangle database in 3-space.
  • the second stage is a determination of the visible triangle fragments in a camera imaging screen.
  • the system utilizes a 3D binary space partition (BSP) tree to accomplish a global visibility sort of the input database.
  • BSP binary space partition
  • the BSP tree sort recursively subdivides the object space and geometry with hyper-planes defined by the surface facets of the input.
  • the scene visibility tree is traversed in a front-to-back order. Each triangle encountered is ray-projected into the camera imaging plane and inserted in a 2D BSP referred to as 2D camera visibility map.
  • a camera visibility map depicts the visible geometry of the scene, as viewed by a camera and projected into its imaging plane (screen). In the event of inter- object occlusions, the visibility map resolves hidden surfaces, depicting resulting visible geometry fragments.
  • a camera visibility map is constructed for each camera in the scene.
  • the object-space camera visibility map is encoded as a 2D BSP tree. In two-dimensions, a BSP tree partitions a camera screen. Edges of the input geometry projected onto the imaging plane define the lines partitioning the space.
  • the screen of the camera is partitioned into regions that are occupied with projected geometry (G-regions), and those unoccupied (U-regions).
  • G-regions regions that are occupied with projected geometry
  • U-regions unoccupied
  • a polygon triangle
  • the clipped visible region of the polygon overlapping the U-region becomes a G-region. That is, it is removed from the visible region of the screen.
  • the image-space algorithm may be implemented entirely in software or utilize 3D graphics hardware if available.
  • the algorithm computes visibility by rasterizing triangles into a 3D graphics frame buffer and also has a z-buffer. Each triangle is rendered with a unique identification value. To determine which triangles are fully occluded, all triangles are rendered into the frame buffer with z-buffer to resolve hidden surfaces. The frame buffer is subsequently scanned and the triangle identification numbers present in the buffer are recorded. Those triangles whose identification numbers do not appear in the frame buffer are deemed fully occluded.
  • the determination of whether a given triangle is occluded, and if so, by which triangles, is as follows. For each triangle, a mask is generated such that a scan of the previously rendered frame buffer will read only pixels contained within the given triangle. The frame buffer is scanned and the triangle is determined to be fully visible if the pixel values read back are only equal to the triangle ID value. If this is not the case, then the triangle is partially obscured, and the triangles which obscure it are identified by the ID values found. Projected areas of triangles (in pixels) are also determined by rendering a triangle and counting the occurrences of its ID value in the frame buffer.
  • each occluded triangle is intersected (clipped) with all those that occlude it.
  • the triangle and intersections are Delaunay triangulated.
  • Fully occluded sub-triangles are marked as not visible. Others are marked visible.
  • the projective area of each visible sub-triangle is calculated.
  • the second stage of the visual reconstruction process is an assignment of a cameras to triangles for texture mapping.
  • Fig. 16 illustrates this process.
  • the angle between triangle normal and camera view direction is calculated.
  • the camera presenting minimum view angle and maximum projective area is selected to map onto a given triangle.
  • Rendering Engine 17 is the visual rendering processor of media processing engine 11, producing all real-time and non-real-time visual output during media creation or playback. During interactive modeling and reconstruction processing with 11, 17 serves "in the loop" as the rendering engine for all graphical display. Rendering Engine 17 reads scene graph and ancillary data unit 14 and constructs visual output representations. Visual output 26 is suitable for direct display on a computer graphics display system or storage on a medium such as a computer hard disk. Rendering Engine 17 output 27 is sent to network 10.
  • Output 26 of 17 includes 2D images, 2D Image mosaic compositions, 2D image collage compositions, 3D texture-mapped object and scene compositions, and compositions of all of the above. The specific output depends on the processing modality of the system and the type of content produced.
  • the current and preferred embodiment of 17 is real-time projective-texture based apparatus and method for rendering a project scene graph on a host computer that supports projective texture mapping in a 3D graphics API such as OpenGL or Direct3D.
  • a real-time capability and use of these graphics API's are not required.
  • 17 utilizes projective texture mapping [2] to texture map 3D geometric constructions directly from the original input images used to model the scene and the camera models recovered by the system.
  • the rendering paradigm is one of treating the cameras in the scene as projectors, loaded with their images, and projecting these images back onto the recovered 3D geometric models.
  • Reconstruction processor 16 subsystem optionally outputs ortho- rectified texture maps and non-projective texture coordinates for rendering using non- affine (orthographic) texture mapping.
  • the rendering engine 17 implements a tiling scheme for loading only those regions of interest of the input images required at any given stage of the rendering.
  • a process of constructing a parametric 3D solid object model from one or more 2D images depicting the object is disclosed.
  • object constructions are compositions of volumetric structure components.
  • modeling engine (PE) 13 and geometry engine (GE) 15 support an extensive and extensible library of structure components, ranging from individual point structures to complex parametric surface structures.
  • a 3D construction process flow diagram is shown as Fig. 14.
  • the process is exemplified with the construction of the domed-arch object depicted in image 1100 of Fig. 6.
  • the arch object is constructed with structure components 50 and 51 of Fig. 4 and structure components 52 and 53 of Fig. 3.
  • the resulting model construction 100 is shown as Fig. 12(f)- Scene graph 375 of Fig. 9(b) depicts the construction hierarchy, with nodes N20, N21, N22, and N23 containing structures 52, 53, 51, and 50, respectively.
  • step ST50 the user imports an object image 24 or downloads an object image 21.
  • Ancillary data might also be imported as data 24, for example in the case of digital acquisition devices that provide such information.
  • object image 1100 of Fig. 6 is downloaded from network 10.
  • a default 3D construction coordinate system is established with camera C100 belonging to image 1100 assuming an arbitrary default 3-space pose and internal parameterization.
  • a model structure is selected and instanced.
  • the user selects a "box" structure from built-in library 18 which is instanced by the system as structure 52.
  • Structure 52 serves as the base of the domed-arch object construction and contains three shape parameters base_width, base_height, and basejiepth.
  • the first structure is spatially constrained to the origin of the 3D construction coordinate system.
  • Fig. 8(a) shows the initial scene construction with structure 52 constrained to the origin of the 3-space coordinate system.
  • the values of structure 52 shape parameters and camera C100 internal and external parameters are initialized to arbitrary default values.
  • the system inserts "base" structure 52 as root node N20 in scene graph 375 of Fig. 8(b).
  • step ST52 through user-interactive input 25 and interface 12, the user interactively places feature annotations in the current input image and establishes correspondence between model structure elements and image features.
  • line segment edge features EF20, EF21, EF22, EF23, EF24 are placed in image 1100. These annotated features are shown in image 1101 of Fig. 6. This set of features is sufficient to identify the x, y, and z extents of the base object.
  • Feature pairs EF20-EF21, EF22- EF23, and EF21-EF24 set x, y, and z dimensional extents, respectively.
  • Annotated features EF22 through EF24 are corresponded by the user to geometry edge elements EE20 through EE24, respectively.
  • image 1100 is marked with structure 52 vertex elements VE10, VE11, VE12, and VE13.
  • the construction parameter space includes nine unknown parameters for camera CIOO ( six pose, one focal length, and two center-of-projection), three unknown shape parameters base width, basejheight, and basejiepth for structure 52, and nine spatial operator parameters (three each for rotation, translation and scale).
  • the spatial operator parameters are fully constrained.
  • To set a relative object scale one or more shape parameters are set to arbitrary default values by the system or user.
  • To set an absolute object scale one or more shape parameters are set to of known real dimensions by the system or user. Parameter values may be imported into the system as ancillary data 22 accompanying the import of an image 21.
  • parameter basejwidth is set to 1.0 units to establish a relative scale.
  • Intrinsic camera parameters may also be set to known calibration values by the system or user.
  • photogrammetric modeling engine 13 solves the parameter space of all unknown geometry structure and camera parameters.
  • modeling engine 13 executes its reconstruction algorithms to recover nine parameters defining camera CIOO and two unknown and unconstrained shape parameters base teight and basejiepth.
  • Fig. 11(a) shows the construction prior to recovery of shape and camera parameters.
  • image 1104 depicts structure 52 base geometry back-projected into image 1100 with unrecovered shape parameters and from the unrecovered camera CIOO.
  • camera and shape parameters are recovered.
  • image 1105 shows structure 52 geometry back-projected into image 1100 with recovered shape and camera parameters. The geometry now aligns with the image.
  • step ST54 the system inquires as to whether geometry engine GE 15 is to evaluate geometric operators between nodes. If no, (or there is only one node, as is the case for the present example) the process proceeds to step ST56. Otherwise, the process proceeds to step ST55, where all unperformed operations are executed by GE 15. In the present example, the user elects to perform geometric operations after the addition of each new structure.
  • step ST56 the system inquires as to whether additional structures are to be added to the present construction. If yes, process flow proceeds back to step ST51 with the selection and insertion of the next structure. Otherwise, process flow proceeds to step ST57.
  • step ST57 the system inquires as to whether additional images are to be added to the project. If yes, process flow proceeds to step ST50 where a new image is loaded and process enables marking the new image and corresponding marked features of the image with existing or new structure elements. Otherwise, process flow proceeds to step ST58.
  • step ST58 the scene graph is traversed and geometry engine 15 executes all unprocessed geometric operators. If in all geometric operators where already executes at step ST54, processing is complete.
  • step ST51 structure 51 is instanced to represent the dome of the object construction.
  • the system inserts "dome" structure 52 as node N22 in scene graph 375 of Fig. 8(c).
  • Spatial operator S 2 o(X) in link LI between scene graph nodes N20 and N21 of scene graph 375 encodes the spatial parameters between structures 52 and 51.
  • Variable parameters of translation, rotation, and scale are explicitly constrained to position the "dome" component relative to the base component, as shown Fig. 9.
  • Link LI contains the geometric boolean merge operator 61 specifying that structure 51 will be combined with base structure 52.
  • vertex element VE30 at the center of sphere structure 50 could be directly equated to the midpoint of the top face of structure 52.
  • Constraints are also placed on structure 51 shape parameters.
  • the sphere shape parameter sphere _radius is set equal the base structure size cbasejiepth. To solve for the dome height sphere _radius2.
  • point feature VF50 is marked in image 1101 of Fig. 6(b) and is corresponded to vertex element VE31 of structure 51.
  • modeling engine 13 solves for the dome height parameter sphere _radius2. If previously solved parameters are not "locked", the engine will optionally resolve them.
  • the boolean merge operation between nodes N20 and N21 is executed by geometry engine 15.
  • Fig. 12(a) shows the construction prior to the geometric operation.
  • Fig. 12(b) shows the construction after to the geometric operation.
  • Box structure 53 serves as the door opening of the arch assembly, as shown in Fig. 9(a). It is inserted as "door" node N22 in scene graph 375 of Fig. 9(b).
  • Link L2 contains the boolean difference operator 63 specifying that structure 51 will be combined with base structure 52.
  • shape parameters doorjlepth and to basejiepth are equated.
  • edge features EF22, EF30, EF28, and EF29 are marked. These are corresponded to edge elements EE22, EE30, EE28, and EE29 in Fig. 9(a).
  • modeling engine 13 solves for the door width and height parameter door jwidth and doorjheight.
  • the boolean difference operation between nodes N20 and N22 is executed by geometry engine 15.
  • Fig. 12(c) shows the construction prior to the geometric operation.
  • Fig. 12(d) shows the construction after to the geometric operation.
  • structure 50 is instanced to represent the arch of the object construction.
  • the system inserts "arch" structure 50 as node N23 in scene graph 375 of Fig. 9(b).
  • Spatial operator S 22 (X) in link L3 between scene graph nodes N22 and N23 encodes the spatial parameters between structures 50 and 53.
  • Link L3 contains the geometric boolean merge operator 61 specifying that structure 51 will be merged with door structure 53.
  • Cylinder structure 50 corresponds to features in image 1101 of Fig. 6. It is attached to the door by equating its midpoint y-extent with the maximum v-extent of door structure 53 and its midpoint x and z extents with those of structure 53.
  • the width of the arch is to coincide with that of the door, so parameter cyljradiusA is equated to structure 53 parameter door vidth.
  • parameter cyljradiusA is equated to structure 53 parameter door vidth.
  • feature point VF60 is marked in image 1101 of Fig. 6 and corresponded to vertex element VE35, shown in Fig. 4(a).
  • the depth of the cylinder is to correspond to that of the base and the door, so parameter cyljength is equated to base structure 52 parameter basejiepth.
  • modeling engine 13 solves for the arch height parameter cylj ⁇ ad ⁇ usB.
  • the boolean merge operation between nodes N22 and N23 is executed by geometry engine 15. Given the hierarchy of the scene graph nodes, the arch structure is merged with the door structure. The merged structure is subsequently subtracted from the base structure. Fig. 12(e) shows the construction prior to the geometric operation. Fig. 12(f) shows the construction after to the geometric operation, which is the final construction. In the present example, with no more structures or images to process, process flow proceeds through steps ST56 and ST57. At step ST58, GE 15 does no geometric processing since all such operations where performed at step ST55. Process flow therefore terminates.
  • MPE media processing engine
  • the phantom cursor is an apparatus and process for acquiring a 3- space camera solution relative to any planar facet of a scene or object depicted in a 2D image.
  • the PC is alternately utilized to solve for structure constrained to a 3-space plane given a known camera relative to the plane.
  • the PC apparatus comprises cursor graphics associated with 2D images, a user interface specification for creating and or modifying the cursor graphics, and an underlying 3-space construction frame and a parameter solution process.
  • the PC apparatus embeds the fundamental geometry and camera construction and recovery processes of the MPE. This includes the annotation of 2-space imagery, the placement of and constraints on 3-space structures and cameras, the correspondence of 2-space features to 3-space elements, and the recovery of unknown parameters.
  • the "phantom" terminology is employed to denote that structure components formed by the PC mechanism may or may not contribute to a construction model output.
  • the cursor graphics represent 2D-feature annotations to be associated with a 2D image.
  • the phantom cursor graphic is an «-sided planar polygon in the plane of the image, consisting of n feature vertices (points) and n feature line segments (edges).
  • n is either a built-in attribute of the system or is explicitly specified by the user.
  • the system supports any number of simultaneous cursor graphics of varying dimension.
  • the 2D features of a PC graphic correspond to elements of a structure embedded in a 3-space construction frame.
  • a quadrilateral structure embedded a reference plane whose elements are corresponded to quadrilateral features in the image plane of a camera, is sufficient for Media Processing Engine 11 to recover the camera relative to the reference plane.
  • Such a construction is also sufficient to recover the shape parameters of the structure embedded in the reference plane relative to a specified camera.
  • a cursor graphic is instantiated in an image.
  • n of the cursor graphic is known
  • a pre-formed cursor graphic of that dimension is instanced.
  • an interactive process is invoked, whereby the user interactively forms the cursor by indicating the corner feature points of the cursor graphics directly onto the 2D image to which the cursor is associated.
  • PGl comprises four feature line segments FI, F2, F3, and F4. The intersections of these feature line segments determine the four corner feature vertexes VFl, VF2, VF3, and VF4 of the main rectangle. The extensions of the feature line segments beyond the intersections provide a visual aid to assist the user in manipulating the cursor.
  • PGl is yet to be associated with a 2D input image.
  • Fig. 17(b) shows a default 3-space construction frame associated with the PGl. The present embodiment defaults the 3-space construction frame to a standard Cartesian coordinate system with x, y, and, z axes oriented as illustrated.
  • Rectangular structure 69 embedded in the reference plane represents the imaging structure.
  • Default camera Cl is initialized to some arbitrary internal and external parameterization.
  • Element vertex VFl is assigned as the first vertex of PS1 and is automatically corresponded to PGl vertex feature VFl. Assuming a clockwise order of the remaining vertices of PS1, all the vertexes and line segments between PGl and PS1 are automatically corresponded. These assignments and orientations are defaults and are fully alterable by the user or the application.
  • a plane in the room scene of image 1200 of Fig. 18(a) is recovered.
  • Fig. 18(b) shows image 1200 with PGl superimposed.
  • Floor plane 303 depicted in the image is to be recovered.
  • the user interactively reshapes phantom graphic PGl superimposed within the image viewing window such that it appears to lie in perspective on the depicted plane of the floor.
  • Fig. 18(c) and Fig. 18(d) show modified cursor graphic PGl reshaped from its original square shape to a quadrilateral in the 2D image plane. This quadrilateral is now the image feature annotation set that relates the projection of the 3D reference plane and embedded structure construction into the image plane of the camera.
  • the 3-space construction of the present example is shown in Fig. 18(e).
  • the x-z plane is designated by the application as the initial reference plane defining floor 303 in image 1200.
  • the reference plane is represented by structure 310 embedded in the x-z plane.
  • the orientation of the initial reference plane is set by either the application or explicitly by the user.
  • PC structure PS1 is embedded in plane 310 and by default is centered on the 3-space world origin.
  • structure PS1 is a
  • Camera system C200 is attached to the input image 1200 and is to it be recovered.
  • PME 13 recovers camera C200 and phantom structure PS1 shape parameters relative to fixed reference plane 310.
  • Fig. 19 shows a flow diagram of the PC process.
  • PC graphic PGl construction reference plane 310
  • 3-space PC structure PS1 3-space camera system C200 are established and initialized.
  • PC graphic PGl feature segments FI, F2, F3, and F4 and feature points VFl, VF2, VF3. and VF4 do not correspond to any feature of input image 1200 - the cursor is "floating" as shown in Fig. 18(b).
  • the system automatically corresponds PC graphic features to PC structure elements.
  • Fig. 18 PGl line segments FI, F2, F3, and F4 are assigned to PS1 edge element? El, E2, E3, and E4, respectively. Alternate correspondences are equally valid as long as they maintain a proper clockwise or counterclockwise feature to edge assignment.
  • the system also allows the user explicitly establish the correspondences if desired.
  • step ST101 through user interface 12 and user-interactive input 25, the user interactively reshapes PC graphic PGl superimposed on image 1200.
  • user interface 12 allows the user to interactively "grab" line segment and point features to reshape the PGl in a "rubber-banding" fashion.
  • Cursor line segment features FI through F4 and point features VFl trough VF4 are repositioned such that PGl appears to lie in proper perspective within the targeted plane depicted in the image. For the present example, this is the depicted floor plane 303.
  • the PC mechanism does not require image feature annotations to correspond directly to particular features seen in associated imagery.
  • step ST101 establishes correspondence between PC graphic PGl, input image 1200, and camera C200. With the correspondence between the PC structure PS1 and PC cursor graphic PCI established by the system, the PC graphic PCI is now considered “attached” as opposed to "floating".
  • parameter constraints are set implicitly by system or explicitly by user. If the plane recovery represents the first geometry-camera relationship established the PC automatically defaults one dimension of structure PS 1 to an arbitrary scale value. This establishes implicit scale for the project. Alternately, partial or full true scale may explicitly set by the user with known or assumed values. For example, for partial scale, the user sets one dimension of rectangle PS1 to an actual known dimension of the scene or object. For full scale, the user sets two dimensions. In general, for an n-
  • n shape parameters may be set.
  • explicit scale the user will typically align PC graphic features to observed features in the image that are of known (or to be guessed) dimensions.
  • photogrammetric modeling engine 13 is invoked to solve for all unknown and unconstrained parameters of the overall PC construction.
  • shape parameters of PS1 as well as the intrinsic and extrinsic parameters of camera C200 are recovered.
  • the system provides a processing modality that will mosaic together any number of images shot from a camera from the same viewpoint.
  • This apparatus and method allows an individual to ready transform an ordinary camera into a wide-angle acquisition and visualization system.
  • media processing engine 11 accepts two or more 2D digital images 24 or 21 under the control of user-interactive input 25 through user interface 12.
  • An example of input 24 is a user downloading images from a digital camera directly onto the host computer system.
  • An example of input 21 is a user downloading digital imagery from a conventional film developing service (e.g. conventional 35mm film) which offers digital output format and delivery directly over the Internet.
  • FIG. 20 A flow diagram of the mosaic process is shown in Fig. 20.
  • the process begins at step ST200 with the establishment of a 3-space mosaic construction frame and imaging plane.
  • the construction frame constitutes the parametric 3D geometric representation of the mosaic.
  • step ST201 the user enters the first input image via 12.
  • the first input image is established as the base image of the mosaic construction.
  • Process flow then proceeds to step ST202 and a system query for entry of another input image.
  • a mosaic comprises two or more images. If at step ST202 only one image has been loaded, process flow automatically proceeds back to step ST201 for entry of another image. If at step ST202 no additional images are required, process flow proceeds to image annotation step ST203.
  • the user identifies, within each image, one or more planar polygonal regions in common with the other image. Such regions are marked in each image with the PC mechanism. Correspondences amongst regions and images are established by formation of PC groups, whereby each PC group creates and shares a common geometric structure embedded in the mosaic imaging plane of the 3-space construction.
  • the first PC group is established.
  • the user identifies a region common to two or more input images.
  • a PC is created and placed by the user clicking on n corner points of an identified «-sided region while the system "rubber-bands" out piecewise adjoining line segments.
  • the system maintains PC and group accounting by mode - all PC's established during a given "form group" mode are equated.
  • groups may be set explicitly by the user or implicitly by an application.
  • FIG. 21 An example of the user-interactive process at step ST203 is shown with the set of input images of Fig. 21.
  • the user inputs three images 1300, 1301, and 1302 at steps ST201 and ST202.
  • image 1300 is entered first, thus becoming the base image.
  • the user visually identifies and selects the center "french door" region common to all three images.
  • Fig. 22 shows input images 1300, 1301, and 1302 with PC graphics superimposed.
  • image 1300 the user marks four comer points of the identified region starting from the lower-left comer point VF5 and proceeding in a clockwise order.
  • a counter-clockwise layout could have been entered, as long as within a group, each PC annotation follows the same clockwise or counter-clockwise orientation.
  • PC graphic PG2 with edge features F10 through F13 as shown.
  • the system automatically extends the polygon edge line segments to span the dimensions of the image. This enhances accuracy and provides an visually aids the user's adjustments to the PC graphic.
  • the user may interactively adjust PC graphic features by moving the edge segments and or the comer points through user-interactive input 25 and interface 12.
  • image 1301 the user marks the identified region starting from the same lower-left point orientation VF9 and proceeding in the same clockwise fashion, producing a PC graphic PG3 with edge features F14 through F17. The same process is carried out with image
  • the system automatically corresponds image 1300 PC feature F10 to image 1301 PC feature F14 and image 1302 PC feature F18, and so on.
  • the construction of a mosaic consists of a common imaging reference plane and structure representing that plane, a PC structure for each PC group embedded in the imaging plane, and a camera system associated with each input image.
  • the PC structure for the first PC group is embedded in the reference imaging plane structure.
  • FIG. 23 A construction for the present example with three input images 1300, 1301, and 1302 is shown in Fig. 23.
  • the mosaic imaging plane is represented by planar rectangular stracture 50.
  • Cameras C300, C301, and C302 correspond to images 1300, 1301, and 1302 and PC graphics PG2, PG3, and PG4, respectively.
  • the first (and only) PC structure, PS2, corresponding to PC group PG2, PG3, and PG4, is embedded in image plane structure 50 with its centroid spatially constrained to 3-space coordinate origin 52.
  • PS2 is a quadrilateral, corresponding to the dimension of each PC graphic in the group.
  • PC structure PS2, shown in greater detail in Fig. 24(a) is comprised of four vertices VE5, VE6, VE7, and VE8 and four edge elements, E5, E6, E7, and E8.
  • the shape of PC structure PS2 is determined by 8 independent parameters xa, ya, xb, yb, xc, yc, xd, and yd.
  • the coordinates of the vertices of PC structure PS2 are a linear combination of these shape parameters as prescribed in Fig. 24(b).
  • an ⁇ z-sided structure contains 2n parameters. If the structure is constrained to be rectangular or square, then the numbers of shape parameters are 2 and 1, respectively.
  • a base image and camera for the first PC group is selected.
  • the base image defaults to be the first image entered at step ST200. In the present example, this is image 1300.
  • the mosaic composition of a set of images requires finding the camera solution of each image relative to a common image plane.
  • the base camera-image pair (C300-I300) is fixed such that its line-of-sight is orthogonal to the image plane (coincident with z-axis) and its point-of-view a fixed distance along the z-axis. In this configuration, the mosaic imaging plane and the image plane of the base camera are parallel.
  • Fig. 24(c) shows how PC structure PS2 is corresponded to the PC graphic group comprised of PC graphics PG2. PG3, and PG4.
  • Each edge element of PS2 corresponds to 3 edge features.
  • edge element E5 corresponds to edge features F10, F14, and F18.
  • step ST204 the shape parameters of the first PC stracture are solved by modeling engine 13. Once solved, the values of these shape parameters are locked.
  • step ST205 an inquiry to determine if additional PC groups are to be added. If yes, the process flow proceeds to step ST206, otherwise the process flow proceeds to step ST208.
  • the user adds one or more additional PC groups.
  • a PC stracture is automatically added to the mosaic imaging stracture for each additional group. These structures are constrained to lie in the imaging plane but are not explicitly constrained in position.
  • step ST208 the shape and position parameters of all unsolved PC group structures and cameras are solved for by modeling engine 13.
  • all three input images are corresponded through a PC stracture PS2. No additional PC groups are added.
  • Cameras C301 and C302 are recovered by modeling engine 13 relative the solution of PS2 and camera C300 at step ST204.
  • visual reconstruction engine 16 composes the set of input images into a single mosaic image.
  • output 26 of media engine 11 is a single image composition of the input image set.
  • the composition of the input images is a projection of the images onto imaging plane 50 from the respective recovered cameras of the input images.
  • the base image is first projected and texture-mapped onto the mosaic imaging stracture. Subsequent camera projections are clipped against regions on the image plane texture-mapped, with only regions previously not texture-mapped rendered.
  • the clipped images are composed using standard edge feathering techniques. In general, any blending and composition methodology may be employed.
  • Rendering Engine 17 displays the resulting composition.
  • Fig. 25 image 1303 shows the resulting mosaic composition for the present example with of input images 1300, 1301, and 1302.
  • output 27 delivers the mosaic content output to the network 10.
  • a mosaic database is three-dimensional and therefore the rendering of the mosaic may leverage the 3D stracture. For example, the entire mosaic imaging plane construction be rotated or translated in a 3D viewing operation.
  • a collage is a composition of a base (destination) image with one or more ancillary product (source) images.
  • the composition process entails "cutting" specified regions of source images and “pasting" them into specified regions in the destination image.
  • Examples of base images include single images representing scenes and/or objects to be modified and image mosaics of such images generated by the system or by an external process.
  • media engine 11 accepts two or more 2D digital images from the user at data input 24 under the control of interface 12. Alternately, images are downloaded into the system at input 21 from network 10. The system outputs 2D image collage compositions 26 for rendering and viewing by the system, for storage, or sends 2D image compositions to network 10 through output 27.
  • the collage process entails the user identification and annotation of "cut from” feature regions in a set of input source images and identification and annotation of corresponding "paste to” feature regions in an input base image.
  • a system solution for source and destination region geometry and camera systems relative to a common collage imaging plane enables projective composition of source collateral with the destination collateral.
  • FIG. 26 A process flow diagram for image collage construction and composition is shown in Fig. 26.
  • a collage is formed with scene image 1200 of Fig.
  • Fig. 29(a) shows the 3-space collage construction frame for current example.
  • the process begins with the establishment of a 3-space collage construction frame and imaging plane.
  • the construction of a collage consists of a common collage imaging plane and stracture, one or more structures embedded in the imaging plane stracture representing destination regions, and a camera system associated with each input image.
  • the 3-space construction frame constitutes the 3D parametric geometric representation of the collage.
  • the 3-space construction frame of the collage apparatus is the default PC construction of Fig. 17(b) with the x-y ( ⁇ - 0) plane as the collage imaging plane.
  • planar rectangular stracture 600 is embedded in the plane and serves as the collage imaging plane stracture.
  • a base image is entered through user interface 12.
  • the process assumes a collage based upon a single base image. If additional scene images are desirable or required, the mosaic process (or other means) are employed to produce a base image that is a mosaic composition of a multiplicity of scene images. Source images may also be mosaic compositions.
  • scene image 1200 is entered.
  • associated camera C200 is established and constrained to a fixed 3-space position and pose parameterization. Its point of view is on the z-axis a fixed distance 601 from the construction frame origin and its line of sight is coincident with the z-axis and pointing toward the imaging plane 600.
  • a source (product) image is imported.
  • carpet image 1500 of Fig. 28 is imported.
  • corresponding camera C500 is instantiated with an unsolved default parameterization.
  • step ST303 the user annotates the base image and the current source image as a PC group.
  • PC graphics are entered on a point-by-point basis or as a pre-formed graphic from built-in library 18.
  • source image 1500 is annotated with PC graphic PG5 to identify the source region of the current source image.
  • Feature vertex VFl is arbitrarily assigned first and the remaining three vertices are marked in a clockwise direction.
  • Scene image 1200 is annotated with PC graphic PG6 to identify the corresponding destination region in the base image resulting in Fig. 27 image 1400.
  • feature vertex VF5 is marked first and the remaining vertices are marked in a clockwise direction.
  • the PC process corresponds VFl to VF5 and the remaining features in the PC group according to the given input topology.
  • a PC stracture for the current PC group is embedded in the collage imaging plane and stracture.
  • the dimension of the PC stracture for the group is the number of vertices of the source region PC. In the present example, the dimension is four.
  • the PC stracture elements are automatically corresponded to base image PC graphic features through projective topological preservation.
  • the PC stracture elements are automatically corresponded to the source PC graphic features since the base and source PC graphic correspondences have been established.
  • Modeling engine 13 solves for the unknown variable shape parameters of the PC group stracture based on its correspondence with the fixed base camera. After the group PC stracture parameters are recovered, they are locked. Modeling engine 13 subsequently recovers the variable intrinsic and extrinsic parameters of all or selected source cameras.
  • PC stracture PS3 is placed in imaging plane stracture 600, as shown in Fig. 27.
  • PC stracture PS3 vertex element VE10 is corresponded to PC graphic PG5 vertex feature VFl and PC graphic PG6 vertex feature VF5.
  • PS3 contains 8 independent shape parameters like stracture PS2 of (a). These variable shape parameters are recovered based on the fixed position and pose of camera C200 attached to base image 1200. With these stracture parameters constrained, camera C500 attached to source image 1500 is recovered.
  • the system queries as to whether additional source images are to be processed. If yes, process flow proceeds back to step ST302 and the input of another source image. If no, process flow proceeds to step ST306.
  • process flow proceeds back to step ST302 with the input of source image 1502 of Fig. 28.
  • Fig. 27 base image 1400 is annotated with PC graphic PG7.
  • Vertex feature VF9 is entered as its first vertex.
  • Source image 1502 is annotated with PC graphic PG8 with vertex feature VF13 as its first entry.
  • PC stracture PS4 is inserted in imaging plane stracture 600. Again, the stracture placed contains 8 independent shape parameters like the stracture PS2 of (a).
  • the system corresponds PC graphic PG7 vertex feature VF9 to PC graphic PG8 vertex feature VFl 3 and PC stracture PS4 vertex element VE20.
  • PC stracture PS4 variable shape parameters are recovered by modeling engine 13 based on the fixed position and pose of camera C200 attached to base image 1200. With these stracture parameters constrained, the variable intrinsic and extrinsic parameters of camera C502 attached to source image 1502 are recovered.
  • reconstruction engine 16 prepares the collage database for composition and rendering. All source images are clipped against their respective PC graphic regions. This constitutes the "cut" portion of the "cut-and-paste" collage processing paradigm. By default, the interior region of a PC graphic is retained as the source imagery to be composed with the base imagery. Alternate selections are made by the user or the application program. In general, any combination of clip regions can be selected.
  • Rendering Engine 17 composes and renders the base image with all source images.
  • Clipped source image regions are composed with their respective base image destination regions by projecting them onto the base imaging plane through their recovered cameras.
  • base image 1200 is projectively texture-mapped onto image plane 600 through camera C200.
  • source image 1500 is projectively texture-mapped onto it corresponding stracture PS3 through camera C500 and source image 1502 is projectively texture mapped onto stracture PS4 through camera C502.
  • source regions may be combined with destination region using any desired blending operation.
  • source pixels replace destination pixels.
  • the final collage is Fig. 30 image 1401.
  • Rendering engine 17 implements multiple image layer composition techniques to resolve hidden surface and hidden object situations during collage composition. Commonly available image region selection algorithms and techniques are incorporated to generate masking layers. These layers are known as alpha channels. During composition, these masking layers determine which pixels of source and destination imagery contribute to the final collage composition. Rendering engine 17 processes alpha channel images as an integral component of its projective composition methods. Rendering engine 17 utilizes some of the same techniques in the rendering of full 3D constructions.
  • Fig. 31 shows room scene image 1600 into which television image 1601 will be collaged.
  • Image 1602 of Fig. 32 shows a collage composition of images 1600 and 1601.
  • image 1602 source "television” pixels of image 1601 are seen obscuring "couch” pixels, a visibly unrealistic and undesirable result. This area is pointed to as region Rl.
  • Image 1603 of Fig. 32 shows an alpha channel image generated by the user with a system incorporated image region selection tool or a standalone image processing tool such as [4].
  • image 1603 the "couch" region (black pixels) is isolated from the remainder of the scene (white pixels).
  • rendering engine 17 projects alpha mask image 1603 from the perspective of the recovered scene image camera onto the collage imaging plane prior to rendering the source region of image 1601.
  • destination pixels are replaced only if their corresponding alpha image pixels of image 1603 are white.
  • Image 1604 of Fig. 33 shows the final result, with the couch pixels preserved.
  • Image 1604 also shows utilization of the alpha channel image 1603 to assist in modifying the depicted color of the couch.
  • the above-described mosaic and collage methods form image compositions based on 3-space constractions with a single imaging plane.
  • the 3D scene construction methods disclosed below extend these concepts to constractions that are assemblages of multiple planar and volumetric structures in 3-space. Scene constructions are assembled from one or more input images.
  • Scene construction methods are presented in the context of an application specific implementation of the current embodiment of the system.
  • the application example given is targeted for the construction of a 3D architectural scene.
  • An example room interior construction is shown.
  • This application and associated methods are just one example of the general applicability of the MPE to a vast range of scene and object construction scenarios.
  • a process flow diagram of a general 3D scene construction process is shown in Fig. 34.
  • a 3D geometric construction for the scene depicted in image 1700 of Fig. 35 is created.
  • the resulting geometric construction is shown in Fig. 36.
  • step ST400 the user imports a scene image through user interface 12.
  • step ST401 the system determines if the current input image is the first. If it is, then process flow proceeds to step ST402. Otherwise, process flow proceeds to step ST403.
  • a PC process is initiated for the recovery of an initial scene reference plane and stracture and camera system for the current scene image.
  • the present application is pre-programmed to begin construction of a room environment from a root floor plane.
  • This "floor-up" method defaults the horizontal x- ⁇ plane of the PC Cartesian coordinate system as the floor reference plane.
  • an application requiring only the recovery of a wall surface might initialize fhe -z or x-y plane as the initial reference plane.
  • the user may explicitly specify the orientation of the initial reference plane.
  • a planar rectangular stracture is automatically placed by the system to represent the reference plane.
  • the reference stracture contains two variable shape parameters set by the system at initialization. The values for these parameters are selected such that the reference plane is large enough to encompass the field-of-view of
  • the size parameters are set to arbitrarily large numbers. These values are either explicitly modified by the user or are procedurally modified by the system once the field-of-view of the designated scene camera is recovered. In the latter case, the application determines if the geometry of the structure is clipped by the image window of the camera, when back-projected into the camera. If so, the system enlarges the geometry until this condition is eliminated. This procedure thus ensures that the size of the structure is large enough to span the extent of the view port of the camera.
  • a PC graphic is placed by the user in the main scene image and a corresponding PC structure is placed in the default floor reference plane.
  • the PC process is executed with modeling engine 13 solving for the variable shape parameters of the PC stracture and intrinsic and extrinsic variable parameters for the camera associated with scene image.
  • the user enters known, guessed, or measured values for PC stracture shape parameters.
  • a scene takes on trae-to-scale proportions if actual measured values are provided.
  • step ST400 scene image 1700 is imported. This is the first input image and process flow proceeds to step ST402.
  • step ST402 planar rectangular structure 900 from built-in library 18 is inserted by the system in horizontal x- ⁇ plane of the coordinate frame.
  • a planar rectangular stracture from library 18 is embedded as PC structure PS10.
  • PS10 is spatially constrained to be centered at the origin of the coordinate frame.
  • PS10 contains two variable shape parameters SI and S2.
  • Camera C700 corresponding to image 1700, is instanced with unsolved default parameters.
  • PC graphic PG10 is placed in scene image 1700, as shown in Fig. 35.
  • PC graphic PG10 In user-interactive input 25 and interface 12, the user interactively shapes PC graphic PG10 in scene image 1700 to appear to lie in the depicted floor.
  • Fig. 36 shows the 3-space construction with floor plane stracture 900, PC stracture PS10, and camera C700 established by the PC process.
  • PC graphic PG10 features F81, F82, F83, and F84 correspond to PC stracture PS10 elements E81, E82, E83, and E84, respectively.
  • the scene construction is calibrated to real world dimensions through
  • image 1700 the user has aligned PG10 such that dimension SI corresponds to the width of the depicted hallway entrance and S2 corresponds the distance between the wall of the hallway entrance and the end of the
  • HI fireplace The user supplies known measured values or guessed values for these parameters.
  • Modeling engine 13 solves for all unknown parameters of the PC construction.
  • Floor structure 900 and scene camera C700 intrinsic and extrinsic parameters are recovered relative to established structure 900 and calibrated to produce the scale dictated by the submitted shape parameters of PC structure 900.
  • Fig. 37 shows the construction scene graph 275 with structure 900 in "floor" node N10.
  • step ST403 the system queries as to whether additional structures are to be added. If yes, process flow proceeds to step ST404. Otherwise, process flow proceeds to step ST407. At step ST404 the next structure of the 3-space construction is selected and placed. Structure selection is explicit by the user or implicit by the application.
  • Planar rectangular structure 902 instanced from built-in library 18 is selected by the user as a "wall” object.
  • the present application is pre-programmed to place “wall” structures in an orthogonal orientation relative to "floor” structures and to build-up from floor structures.
  • Structure 902 is inserted into scene graph 275 as "left-wall" node N12, a child of floor node N10.
  • the system constrains the minimum jy-extent of plane 902 to the surface of floor plane structure 900.
  • structures added to the scene are initialized to default or procedurally generated sizes.
  • Structure 902 has two shape dimensions S3 and S4, corresponding to wall vidth and walljteight, respectively. Unless explicitly overridden by the user, the application sets walljwidth and walljteight to values large enough that the extents of these structures cover the f ⁇ eld-of view of the 2D image window as viewed by the scene camera C700.
  • step ST405 the full position and shape parameterization of an added structure is resolved.
  • the user annotates features in the scene image that correspond to elements of the new structure.
  • the system will request information from the user specific to the context of the construction underway.
  • the system requests the user annotate input image 1700 with an edge feature line segment that indicates where the wall and floor meet.
  • image 1700 the user annotates line segment F70.
  • the system knows this edge element belongs to a "floor-wall" juncture, so image feature F70 is automatically corresponded to structure 902 edge element E70.
  • floor plane 900 and camera C700 are known, the F70-E70 correspondence pair is sufficient to fully place structure 902.
  • the system queries the user to provide image annotations for the left, right and top edges structure 902 to resolve its shape parameters. If none are provided, as is the case here, then default or system procedurally derived dimensions are retained.
  • modeling engine 13 solves for unknown parameters of the construction.
  • the system locks all previously solved for parameters.
  • the system allows for all or select groups of solved parameters to be re-solved.
  • parameters pertaining to reference plane 900 and camera C700 are locked.
  • the unknown and unconstrained parameters of spatial operator S 1 2CX) in the link between scene graph nodes N10 and N12 are recovered.
  • Planar rectangular structure 903 is placed orthogonal to floor structure 900.
  • the position of plane 903 relative to plane 900 is established with user-supplied feature annotation F71 in image 1700.
  • Image feature F71 is automatically corresponded to structure 903 edge element E71.
  • the user also annotates image feature edge segment F72 corresponding to structure 903 element E72 to place the right edge of the wall 903.
  • Structure 903 is inserted into scene graph
  • the next structure added to the construction is three-parameter volumetric box structure 904, which becomes “fireplace " node N13 in scene graph 275, a child of left-wall node N12.
  • the application constrains the minimum y-extent of the box structure to floor structure 900 and set "box-height" dimension S5 equal to the height value of wall structure 903.
  • the application also sets the minimum x-extent of structure 904 to the plane of left-wall structure 902, constraining the back of the fireplace to the face of the wall.
  • Modeling engine 13 solves for all remaining unconstrained and unsolved parameters, including spatial operator S 13 K) in the link between scene graph 275 nodes N12 and N13.
  • a scene construction contains one or more scene images.
  • the user may elect to annotate an image at any time, in which case process flow proceeds to step ST405.
  • the system queries the user as to whether a scene image is to be annotated. If affirmative, process flow proceeds to step ST5. If negative, process flow proceeds to step ST408, where the system queries the user as to whether a scene image is to be added to the construction.
  • step ST408 if affirmative, process flow proceeds back to entry point step ST400.
  • the user may add scene images at any time, at which point process flow proceeds to step ST400.
  • the addition of an image also means the addition of a camera system attached to the image.
  • process flow proceeds to step ST403 through step ST402.
  • step ST408 if negative, process flow proceeds to step ST409.
  • the system performs all geometric operations on the scene construction.
  • the scene graph of the scene construction is traversed and the geometric operations to be performed between structures are extracted from the links of the scene graph and are executed by GE 15.
  • a traversal of Fig. 37 scene graph of 275 shows that all links between structure nodes contain boolean union operator 60.
  • the scene construction of Fig. 36 depicts the scene 3D construction after traversal of the graph and execution of all union operators.
  • Intelligent objects are object constructions whose functional components are structurally modeled and subject to physical simulation through variation of the variable parameters of their composition.
  • An example of an intelligent object is a cabinet construction containing a drawer construction that is parameterized to slide open and close.
  • Intelligent scenes analogous to intelligent objects, are scene constructions whose functional components are structurally modeled and subject to physical simulation through variation of the variable parameters of their composition.
  • An example of an intelligent scene is a room interior construction containing a door construction that is parameterized to swing open and close.
  • the system provides seamless integration, visualization, and simulation amongst the various construction modes and types. This includes the integration of image mosaics, image collages, 3D scene constructions, and 3D object constructions. Constructions are integrated within a single project or as the coalescing of a number of separate projects. The latter scenario is typically implemented using a client-server processing model, whereby construction components are interchanged between a multiplicity of processing nodes and projects.
  • Fig. 39 illustrates a media integration and simulation process flow.
  • step ST900 the system queries as to whether a valid scene graph database resides in unit 14. If true, process flow proceeds to step ST904 and the import of an insertion database. If not, process flow proceeds to step ST901 where a decision is made whether to construct a new scene database or load on existing saved scene database. If the user elects to create a new database, process flow proceeds to the 3D scene construction process at step ST902. Otherwise, process flow proceeds to step ST903 and the import of a scene database into unit 14.
  • an object database is imported and pre-processed.
  • Imported databases range from complete projects that include imagery, geometry and camera models, and ancillary data to sparse projects that might include only one of these components.
  • the receiving application might import imagery of an object but not the object geometry; depending on the application configuration, geometry for a construction related to an imported image may be internally sourced from built-in library 18. This reduces the amount of data transferred over network 10.
  • step ST905 the method of object placement is selected.
  • the placement of an object is either under the control of the application or the discretion of the user. If initial placement is not under the control of the user, process flow proceeds to step ST907 where objects are assigned an initial position within the 3-space coordinate system procedurally determined by the application program. Otherwise process flow proceeds to step ST906 where the type of user-interactive object placement is selected.
  • Object placement is established by the user object with a point-and-click mouse interface through user input 25 and interface 12.
  • the anchoring of source and destination constructions is accomplished by selection and correspondence of features of source images and/or elements of source models to features of the destination scene images and/or elements of destination scene models. For the source, the user interactively selects one or more image feature annotations or object elements as anchor reference points.
  • Modeling engine 13 solves for all unknown and unconstrained parameters defining the shape and spatial interrelationships between merged construction components.
  • Scale factors between source and destination models are established by the method of placement employed and the known and recovered values of the established parameter space.
  • the system employs two primary methods of placement, true-to-scale and force-fit-scale .
  • True-to-scale processing is based on the use of explicitly known values of source and destination model parameters.
  • Force-fit-scale processing is based on the use of implicit and recovered values of source and destination model parameters governed by placement constraints.
  • step ST906 the type of user-interactive placement is selected. If the "explicit scale” processing is selected process flow proceeds to step ST908. Otherwise process flow proceeds to "force-fit scale” processing at step ST909.
  • true-to-scale object insertion is executed.
  • source and destination model dimensions are known or assumed.
  • the user identifies and equates feature and element anchors in source and destination imagery and geometry.
  • Modeling engine 13 solves for the values of unknown spatial operators and camera models.
  • the absolute scale between merged source and destination models is preserved.
  • a typical use scenario of explicit scale is when a scene of known true dimensions is inserted with an object of known true dimensions.
  • force-fit scale object insertion is executed.
  • a destination region is specified which allows modeling engine 13 to size as well as position the source model into the destination model.
  • the scale factor between merged source and destination constructions is established by the proportions of the destination
  • a typical use scenario for this method is the insertion of an object of unknown dimensions into a scene.
  • a new scene database is constructed.
  • Process flow proceeds to step ST902 and the 3D construction process of Fig. 34is executed.
  • the PC construction process of Fig. 18 is executed.
  • the 3-space construction of Fig. 42(c) is produced, with recovered floor plane structure 700, embedded PC structure PG20 and recovered camera model C200 for image 1200.
  • Data structures unit 14 contains scene graph 575 with a single node NlOO containing floor structure 700, as shown in Fig. 42(a).
  • the scene is scaled to true measured dimensions through the PC process, with the PC graphic aligned to known image feature reference points and the actual measured dimensions of those features entered for the corresponding parameters of the PC structure.
  • an imported object database comprises rug source image
  • the present application example is pre-configured to import "rug" databases. As such the system is configured to internally supply planar rectangular structures from built-in library 18 for "rug" object constructions. With the import of image 1500, camera model C500 is established. In the present example, no camera model parameters are imported with image 1500; camera C500 is internally initialized with no known model parameters assumed.
  • the application automatically assigns planar rectangular structure 750 from built-in library 18 to incoming PC graphic PG6, establishing the geometric model of the rug object. Vertex element VE20 of object structure 750 is identified as the correspondence to PG6 vertex feature VF5. A direct correspondence between all PG6 edge and vertex features and structure 750 edge and vertex elements is established.
  • the user elects to interactively place the rug object.
  • the user elects to place the rug using its imported true dimensions rugjength and rugjwidth.
  • the user places cross-hair image features 933 in scene image 1200 to establish the destination anchor point for the rug object on the depicted floor, as shown in image 1800 of Fig. 40.
  • the user selects PC graphic
  • PG6 feature point VF5 directly on image 1500 of Fig. 28, or vertex VE20 directly on object geometry 750.
  • the application also texture maps image 1500 onto geometry 750, allowing the user to select VF5 and VE20 concurrently and directlv from a 3D texture-
  • Modeling engine 13 solves for all unknown camera and spatial operator parameters to place rug structure.
  • the imported rug shape parameters rugjength and rug vidth explicitly specify the true size of the rug object within the scene of known dimensions.
  • Fig. 42(c) shows rug object structure 750 inserted and constrained to floor structure 700. Two cameras are shown. Camera C200 for scene image 1200 and camera C500 for rug image 1500.
  • node N101 containing structure 750 is inserted into scene graph 575 as a child of floor node NlOO, reflecting the insertion of the rug object into the scene.
  • the spatial relationship between structures NlOO and N101 is given by the spatial operator S ⁇ o(X) in the link between the nodes.
  • the values for the variable parameters of S ⁇ o(X) are established by explicit geometric constraints and computation of modeling engine 13.
  • the translation component ofSjo(X) is set to zero, constraining structure 750 to lie in the plane of parent structure 700.
  • the x and z translation and y rotation components of S ⁇ o(X) are recovered by modeling engine 13 to place the rug at anchor point 933, as shown in image 1801 of Fig. 40.
  • step ST909 An alternate example demonstrating the force-fit-scale processing of step ST909 is disclosed.
  • the method combines destination and source model creation and integration through a shared PC process. Combined is the PC process for finding a camera model and reference plane in a 3-space construction for an input scene image and a "force-fit" procedure for merging additional 3-space constructions.
  • the user interface procedure is similar to that of the disclosed 2D image collage process.
  • step ST900 process proceeds to step ST901.
  • step 901 the user elects to create new geometry and process flow proceeds to step ST902.
  • a destination reference plane is selected by the user and a PC process is initiated for the recovery of the initial destination scene reference plane, structure, and camera system relative to the current scene image.
  • the application could be more specifically tailored - such as a "place rugs on floors” or a "place pictures on walls” application, in which case the destination orientation is a built-in attribute of the application.
  • a PC graphic is placed in the scene image and shaped by the user to define the destination region into which an object to be inserted.
  • a corresponding PC structure is placed in the selected reference plane and structure.
  • Fig. 43 shows the 3-space construction.
  • the user explicitly indicates that the destination of an object is a horizontal "floor” plane and the application defaults the horizontal x-z plane of the PC Cartesian coordinate system defaults as the floor reference plane.
  • Planar rectangular structure 800 from library 18 is inserted by the system in horizontal x-z plane of the coordinate frame. If, for example, the picture product of Fig. 28 image 1502 is to be placed within a vertical wall structure in the same scene, the user would explicitly specify a vertical "wall” plane and the application would default the horizontal y-z plane of the PC Cartesian coordinate system defaults as the wall reference plane.
  • Scene image 1200 of Fig. 18(a) is annotated with PC graphic PG5 to identify the destination region in the scene image, as shown in image 1400 of Fig. 27.
  • Feature vertex VFl of PG5 is marked first and the remaining PG5 vertices are marked in a clockwise direction.
  • planar rectangular PC structure PS30, corresponding to PG5 is placed centered about the coordinate system origin.
  • Camera C200 of destination scene image 1200 is initialized with unsolved default parameters.
  • the user Through user-interactive input 25 and interface 12, the user interactively shapes PC graphic PG5 in scene image 1200 such that the PC graphic region represent the outline of the region to be occupied by a source object or components of a source object, relative to the reference structure, and as viewed in perspective.
  • an object database is imported and pre-processed.
  • the imported object database consists of rug product image 1500 of Fig. 28 and corresponding PC graphic PG6. Values for shape parameters rugjength and rug vidth may or may not be imported. No camera model parameters are imported with image 1500.
  • the application example is pre-configured to import "rug" databases. As such, the system is configured to internally supply planar rectangular structures from built-in library 18 for "rug" object constructions. In "force-fit" mode, the application defaults to using the current PC structure geometry. In the present example, this is PS30. The application automatically assigns planar rectangular structure PS30 to incoming PC graphic PG6, establishing the geometric model of the rug object. Vertex element VE30 of object structure PS30 is identified as the correspondence to PG6 vertex feature VF5. A direct correspondence between all PG6 edge and vertex features and structure PS30 edge and vertex elements is established.
  • step ST905 the application is programmed to proceed to step ST906 and then to step ST900.
  • step ST909 "force-fit" processing is executed.
  • the user corresponds one or more image features of source PC graphic PG6 to one or more features of destination PC graphic PG5 or to one or more geometric elements of PC structure PS30.
  • the user clicks on PG6 vertex feature VF5 and then on PG5 vertex feature VFl to fully establish the source region to destination region correspondence.
  • Modeling engine 13 solves for the variable shape parameters of the PC structure PS30 constrained within plane 700 and intrinsic and extrinsic variable parameters for cameras C200 and C500 such that rug source region PG6 is fits into destination region PG5.
  • the relative scale between the rug and the scene is set by the annotation of the rug outline in the scene.
  • the overall scale of the scene and inserted object is determined by the constrained or recovered values of PS30 variable shape parameters.
  • This scale may arbitrary, if arbitrary default values are established for the parameters.
  • This scale may also be based on known imported dimensions of either the scene or the rug. In the latter case, in the event the known true dimensions corresponding to the annotation region in the destination image coincide with the known true dimensions of the source region, the force-fit scale becomes a true-to-fit scale.
  • Interactive Media Simulation Methods Interactive visual and physical based simulation operations can be provided between all modes and types of scene and object constructions.
  • the system can incorporate visual simulation features and techniques available to a 3D computer graphics system.
  • Simulation is controlled by the manipulation of variable parameters of construction through user-interactive input 25 through interface 12 or application programming.
  • the variable parameters of spatial operators S(X) linking geometric structures are static or dynamic.
  • static spatial operators specify rigid spatial positioning between components of an object or scene.
  • dynamic spatial operators enable and specify the simulation and animation of functional components of objects and scenes.
  • structure shape parameters Another example is the variation of structure shape parameters. This enables simulation features such as a review of various sizes and shapes of an object within a scene construction.
  • Fig. 38(a) shows an example intelligent object assembly constructed by the system.
  • the assembly contains dynamic spatial operators.
  • the model assembly consists of main box structure 90, box cover structure 91, and box drawer structure 92.
  • the object assembly is encoded as scene graph 300 with structure 90 in node N15, structure 91 in node N16, and structure 92 in node N17.
  • Spatial operator S ⁇ (X) between nodes N15 and N16 specifies the spatial relationship between the box and its cover.
  • Spatial operator S X) between nodes N15 and N17 specifies the spatial relationship between the box and its drawer.
  • Composed within operator S 6 (X) is cover jrotatejz parameter 94 that specifies rotation of the cover structure about the z-axis linking the cover and the box.
  • drawjransjc parameter 93 that specifies translation of the drawer structure along the x- axis relative to the drawer coordinate frame. Physical-based modeling attributes are also attached to the spatial operators.
  • Variable parameters cover j-otate _z 94 and drawjransjc 93 are manipulated through user-interactive input 25 and interface 12 and application programming to simulate the functionality of these components of the model assembly.
  • Interactive simulation functions of the system include the spatial repositioning of scene models and object models relative to each other.
  • the rug object of Fig. 28 image 1500 is interactively re-positioned within the floor plane of the scene to simulate various placement scenarios.
  • the scene is represented by scene graph 575 of Fig. 42 (b).
  • Repositioning of the rug object is accomplished by interactive variation of the variable parameters of S ⁇ o(X) while retaining its fixed constrained parameters.
  • Fig. 40 image 1801 shows the rug within the scene at its user established anchor point 933.
  • Fig. 41 images 1802 and 1803 are two display frames rendered from rendering engine 17 showing the rug object displaced along the floor from its initial anchor point 933.
  • a client-server processing and communication model suitable for deployment of the MPE and methods over a wide range of network-based applications.
  • the MPE and methods are not limited to such an implementation.
  • one or more client processing nodes and one or more server processing nodes are connected to a computer network, such as the Internet.
  • a client node CN100 is a computer system or other network appliance connected to a computer network. It is capable of communicating with one or more server nodes and other client nodes on the same network.
  • a server node SN200 is a computer system or network appliance connected to a computer network It is capable of communicating with one or more client nodes and other server nodes on the same network Client nodes and server nodes embed MPE 11 Client node CN100 receives and sends and digital information 802 from and to network 10
  • Digital information 802 received from network 10 is comp ⁇ sed of 2D images 21, 3D geomet ⁇ c models 20, and ancillary data 22
  • Digital imagery 21 is downloaded to a client node from a system server node, another system client node, or any other node on network 10
  • Geomet ⁇ c models 20 are paramet ⁇ c and non-paramet ⁇ c, and downloaded from a server node SN200 or other client node CN100 and generated by 11 at those nodes
  • Paramet ⁇ c models generated by 11 are structure assemblages interchangeable between client and server nodes.
  • Paramet ⁇ c and non-paramet ⁇ c models generated by other means may also be downloaded from any network node at input 20
  • Data 802 sent to network 10 is comp ⁇ sed of user 2D images 24 and user 3D paramet ⁇ c models 23.
  • Digital input 804 is digital information a client node receives from a user This input includes user interactive input 25, user digital images 24 imported from sources such as digital cameras and analog-to-digital scanners, and 3D geomet ⁇ c models 23 imported from external medium such as CDROM It also includes system project databases and ancillary data 28
  • a server node SN200 receives and sends digital information 803 from and to network 10
  • Digital information 803 received from network 10 is comp ⁇ sed of 2D images 21, 3D geomet ⁇ c models 20, and ancillary data 22.
  • Digital imagery 21 is downloaded to a server node from a system client node, another system server node, or any other node on network 10
  • Geomet ⁇ c models 20 are paramet ⁇ c and non-paramet ⁇ c, and downloaded from a server node or other client node and generated by 11 at those nodes
  • Paramet ⁇ c and non-paramet ⁇ c models generated by other means, such as other modeling software programs or hardware scanning devices, may also be downloaded from any network node at input 20
  • Digital information 803 sent to network 10 is comp ⁇ sed of user 2D images 24, and user 3D paramet ⁇ c models 23.
  • a server node receives digital information 805 from a user This includes user interactive input 25, digital images 24 imported from sources such as digital cameras and analog-to-digital scanners and 3D geometric models 23 imported from external medium such as CDROM. It also includes system project databases and ancillary data 28.
  • a client node executes client-side application software CA based on the MPE and processing methods disclosed.
  • a client node downloads CA from a server node on network 10 or from other data storage and delivery medium such as CDROM.
  • the client-side application program CA embeds some or all of media processing engine 11 capabilities and features.
  • CA is capable of generating, composing, and visually rendering 2D image mosaics, 2D image collages, and 3D object models, 3D scene models from 2D digital images based on the disclosed system and methods.
  • CA is capable of dynamic visual and physical-based simulation of 3D object and scene models generated within the node, received from the network or other sources, and compositions of node generated and node imported content.
  • a server node executes server-side application software SA based on the MPE and processing methods disclosed.
  • the server application program SA embeds some or all of the media processing engine 11 capabilities and features.
  • SA is capable of generating, inter-composing, and visually rendering 2D image mosaics, 2D image collages, 3D object models, 3D scene models from 2D digital images.
  • SA is capable of dynamic visual and physical-based simulation of 3D object and scene models generated within the node, received from the network or other sources, and compositions of node generated and node imported content.
  • server nodes are responsible for disseminating client-side application software CA components of 11 executed by client nodes to client nodes.
  • the components and type of processing carried out by individual client or server nodes and the data transactions between client and server nodes is a function of the target application.
  • the disclosed merchandising system connects product consumers, (e.g. shoppers, buyers) with product purveyors (e.g. manufacturers, retailers).
  • product consumers e.g. shoppers, buyers
  • product purveyors e.g. manufacturers, retailers
  • the system may operate as a stand-alone application or be integrated as a component in an existing online e-commerce system.
  • Fig. 50 The described embodiment of the e-commerce merchandising system is shown in Fig. 50.
  • product shoppers are associated with client nodes CN100 and product purveyors are associated with server nodes SN200.
  • Client and server processing nodes may also operate in stand-alone mode, removed from network 10.
  • SELL nodes are the e-commerce web sites of product manufacturers, retailers, and the likes
  • SHOP nodes are consumers and the like with an online personal computer or other web-browsing enabled device.
  • a SELL node functions as a content creation station, a content server, and a program server.
  • a SELL node implements the full range of media processing engine 11 digital content creation, visualization, and dissemination capabilities.
  • SELL nodes distribute (serve) merchant-tailored processing engine 11 program components S-PROG to SHOP nodes, enabling SHOP nodes with processing engine 11 programs C-PROG.
  • SHOP nodes import their application software C-PROG from other mediums, such as CDROM.
  • SELL nodes share their content and project databases 20, 21, 22, and 27 with other SELL nodes.
  • SELL nodes create, package, and distribute 2D and 3D digital representations and ancillary information of the products and services associated with the purveyor.
  • Such media includes all combinations of 2D product images, 3D product models, form and function parametric data, and other ancillary product information.
  • Ancillary information includes media typically associated with product catalogs and brochures as well as video and audio presentations.
  • Three principal media packages created and served from SELL nodes to client SHOP nodes are Intelligent Image Packages IIP, Intelligent Object Packages IOP, and Intelligent Ancillary Data packages
  • Both SELL and SHOP nodes may also distribute all forms of data individually and not as a package.
  • Intelligent image packages IIP are 2D digital images or image compositions packaged and served with parametric data that enable a full range of 2D and 3D visualization and simulation capabilities at both SELL and SHOP nodes.
  • a product purveyor offers the rug product of image 1500 of Fig. 28 in several choices of patterns and size.
  • the purveyor SELL node can serve an intelligent image package IIP containing one or more images of the rug depicting color and pattern options along with parametric shape and color option data.
  • Intelligent Object Packages IOP are packages of data associated with 3D object and scene constructions, including scene graphs, geometric structures, texture maps, camera models, and parametric data controlling construction form and function. IOPs enable a full range of 3D visualization and simulation capabilities at both SELL and SHOP nodes.
  • a SELL node offers cabinet product 90 depicted in image 1900 of Fig. 44.
  • An intelligent object construction of cabinet 90 is shown in Fig. 38, with a single parametric drawer component 92 and cover component 91 modeled.
  • cabinet 90 is served by the SELL node with its scene graph 300, imagery 1900, geometric structures, camera models, shape parameters, and function parameters (draw_trans_x, cover_rotate_z).
  • Intelligent Ancillary Data Packages IDP are portfolios of ancillary information related to products and services distributed from SELL nodes. Ancillary information includes textual, audio, or video descriptions of products and services.
  • An IDP is packaged and served with IOPs and IIPs or separately.
  • IDP information is presented by the system through graphical display mechanisms.
  • the primary IDP displays are 2D image overlay "pop-up" mechanism
  • a PUP2 is a 2D image overlay similar to 2D graphical overlay and dialog mechanisms common to windows-based applications.
  • a PUP3 is a 3D a texture-mapped geometric "billboard" object that is embedded and rendered as an additional construction component of a 3D scene.
  • PUP2 and PUP3 mechanisms are attached to scene graph unit 14 nodes of 2D image compositions and 3D object and scene constructions. SELL nodes generate, store, and disseminate PUP2 and PUP3 displays.
  • Fig. 30 shows collage composition image 1401 with PUP2 graphic overlay PUP2-10 attached to the rug object.
  • Fig. 29(b) shows corresponding collage construction scene graph 250 with PUP2-10 attached to rug node N301.
  • Fig. 47 shows an example PUP3 graphical pop-up display PUP3-30.
  • the system allows the SELL node to create or import 3D geometry to represent a PUP3.
  • the system is not limited to any particular PUP3 display geometry.
  • product information is displayed on one face of the display geometry.
  • Fig. 48 shows scene image 1990 with 2D overlay display PUP2-20 attached to wooden CD cabinet product 90 and 3D pop-up display PUP3-30 attached to wooden storage box product 91.
  • Fig. 38(b) shows PUP3-30 attached to node N15 of scene graph 300.
  • PUP2 and PUP3 mechanisms also serve as launch points for associated IDP media.
  • user interactive input 804 the shopper launches video and audio presentations about the product and the merchant by clicking on a PUP object or menus displayed on a PUP object.
  • PUP2 and PUP3 mechanisms also serve as portals for online product purchasing and customer tracking transaction systems.
  • PUP mechanism connects to a SELL node's purchasing and customer tracking systems
  • PUP2 and PUP3 displays become the graphical user interface to those systems.
  • Rendering engine 17 renders PUP displays in a scene graph when they are activated.
  • PUP displays may be re-positioned by the user or automatically by the application program.
  • a PUP2 may move with the object it is attached to as a translation in the plane of the image.
  • a PUP3 may move to any orientation within the 3-space coordinate system of the construction.
  • a PUP3 remains attached to a selected reference point of its associated 3D object and also rotates as a billboard to maintain a consistent orientation relative to the current viewer position.
  • This implementation is analogous to dynamic 3D billboards implemented in 3D graphics visual simulation systems.
  • SHOP nodes download media processing engine 11 "client-side" processing components from SELL server nodes. Some or all of media processing engine 11 functional components are downloaded over 10. The selection of components C-PROG downloaded depends on the application environment and is configurable by the system. SHOP nodes may also import C-PROG processing components from other digital data mediums such as CDROM.
  • SHOP nodes create and integrate 2D and 3D digital environment and product representations and information. Central to their role in a merchandising environment is enabling a shopper to acquire and reconstruct their application environment and readily integrate and simulate product constructions from SELL nodes. In addition, a SHOP node may also generate product 3D constructions directly from 2D imagery. When such information is either not available from a product purveyor or is not applicable to the circumstances. A shopper can acquire documented and undocumented digital imagery from sources such as the Internet, digital cameras, and scanners, and transform them into 3D representations. This extremely flexible program and processing distribution model allows processing and data distribution to be efficiently tailored by the requirements of the commerce environment.
  • SHOP nodes download product-specific 2D digital images 21, scene graphs and 3D texture-mapped models 20, and project databases and ancillary digital data 22 from SELL nodes or optionally from other SHOP nodes over network 10.
  • SHOP nodes import user 2D digital images 24 and 3D models 23 from directly from digital acquisition devices, such as digital cameras and scanners. SHOP nodes may also download user-specific digital imagery 24 and 3D models 23 from network 10 or other medium such as CDROM. For example, a user might photograph a scene or product with an analog camera and employ a processing service that develops and digitizes film and delivers the digital images to the user via the Internet or CDROM. SHOP nodes employ the disclosed media integration methods to integrate user generated constructions with downloaded or imported SELL node constructions.
  • SHOP nodes employ the simulation methods enabling SHOP nodes to simulate form and function of SELL node products within their environments.
  • simulation includes exploration of product size, fit, color, and texture options as well as product functionality (e.g. open-close doors, turn on-off lights, test switches).
  • product functionality e.g. open-close doors, turn on-off lights, test switches.
  • a home furnishings merchant displays items for sale at its e-commerce web site on Internet 10.
  • the site is enabled with the merchandising system as a SELL server node SN200. Displayed at the merchant site is the rug item depicted as image 1500 of Fig. 28.
  • An online Internet shopper visits the merchant SELL site with interest in purchasing the rug. From the merchant site the shopper selects an option to visualize the rug in her home. This selection launches the download of a merchant-tailored client-side
  • the shopper seeks to visualize rug 1500 in her living room depicted in Fig. 18(a) image 1200.
  • the shopper acquires image 1200 with a digital camera and downloads it as user data 24.
  • the shopper takes multiple snapshots and downloads them as user data 24.
  • the shopper employs the image mosaic processing features of media processing engine 11 to stitch the images together.
  • the shopper selects rug image 1500 from the SELL site, triggering a download of merchant product media from the SELL computer to the SHOP computer.
  • the merchant SELL node transfers an intelligent image package IIP for the rug product that includes three product images 21, one for each rug color scheme offered, six size parameters denoting the dimensions of three rug sizes offered, and PC graphic PG6.
  • the SELL node also transfers an Intelligent Ancillary Data Package IDP containing PUP2- 10. No 3D object geometry 20 is transferred from SELL site to SHOP; rug geometry is generated by the SHOP node.
  • the shopper also takes her digital camera to a local retail store and acquires artwork image 1502 of Fig. 28 to visualize it in the same living room with the rug. She imports image 1502 from the digital camera into her computer as user data 24.
  • the shopper employs media processing engine 11 configured in image collage construction mode to produce the collage of scene 1200 with rug image 1500 and artwork image 1502, resulting in image 1401, shown in Fig. 30.
  • the SHOP node application allows the shopper to click on collage product items to scroll through the insertion of the three pattern options.
  • the SHOP node also scales the collage geometry according to the merchant supplied size parameters, allowing the user to select and visualize the various rug size offerings. When selected, PUP2-20 is displayed to provide online product information and launch the SELL node purchasing transaction system.
  • the shopper of Example 1 wants to interactively simulate placement of the rug in various positions on the floor within the living room scene.
  • the shopper employs the system 3D scene construction mode.
  • the shopper selects point VF5 on the rug in image 1500 and anchor point 933 on the floor in image.
  • the system automatically places and dimensions the rug on the floor with the true rug dimensions downloaded with the merchant IIP.
  • the shopper interactively repositions the rug to various locations on the floor and scrolls through the various rug sizes, patterns, and colors.
  • the system automatically and seamlessly interchanges IIP supplied shape parameters, texture maps, and color data.
  • a shopper of home furnishings visits a merchant e-commerce web site on network 10.
  • the merchant site is enabled as a SELL server node SN200.
  • the shopper selects an option at the merchant site to visualize products in his home. This selection launches the download of a merchant-tailored media processing engine 11 software S-
  • PROG from the merchant SELL site to the shoppers' computer system on network 10.
  • This transaction enables the shopper as a SHOP node CN100 with media processing engine 11 software C-PROG.
  • Displayed at the merchant site is pine storage box 91 depicted in Fig. 44 image 1901.
  • the shopper selects image 1901 and the SELL node transfers an IOP and an
  • the IDP for the product to the shopper SHOP node.
  • the IOP transferred contains the product scene graph, geometric structures, camera models, texture map imagery, and parametric shape and function parameters.
  • the IDP contains graphic information display PUP3-30.
  • the shopper visits web site of another merchant on network 10.
  • the second merchant site is also enabled as a SELL server node SN200.
  • Displayed at the merchant site is CD cabinet 90 depicted in Fig. 44 image 1900.
  • the shopper selects image 1900 and the merchant SELL node transfers an IOP and an IDP, including PUP2- 20, for the product.
  • the shopper visits a third web site on network 10.
  • the merchant displays wood cabinet 93 as image 1903 of Fig. 45.
  • This merchant site is not enabled as an SELL node, so the shopper downloads only image 1903 using the image download facility of his web browser.
  • the shopper manually records the advertised dimensions of the product for future manual entry into the SHOP node system.
  • the shopper takes a picture of the room he has in mind for the products.
  • the shopper acquires scene image 1700 of with a conventional camera and has the film developed through a service that provides digital images returned over the Internet.
  • the shopper downloads his image 1700 as user data 24 over network 10 from the film service.
  • the shopper visits a local retail store and acquires photographs of the mirror product of image 1904 of Fig. 45 and the artwork product of image 1502 Fig. 28. No dimensions for these items are obtained. These photographs are processed into digital images and downloaded into the SHOP node in the same manner as scene image 1770.
  • the shopper produces the 3D construction from scene image 1700.
  • the shopper supplies actual room dimensions that correspond to the dimensions SI and S2 of PC structure PS10.
  • the room scene geometry and camera model are calibrated to true scale. If a greater extent of the room scene is desired, the shopper would acquire more images of the room and continue with the 3D scene construction process in multi-image mode.
  • the shopper constructs a parametric 3D texture-mapped model for brown cabinet 93.
  • the parametric model is produced true scale using the product dimensions recorded by the shopper.
  • the shopper then uses the construction "true-to-scale” object insertion process to insert SELL node supplied 3D models for pine storage box 91 and CD cabinet 90 and shopper constructed 3D model for brown cabinet 93 onto the floor of the shopper constructed 3D scene.
  • the "force-fit” insertion process is employed to place mirror 94 and the artwork of image 1502 on the walls of the 3D scene construction. This entails the user placing a PC graphic in each product image as the product source regions and a corresponding PC graphic on the wall locations desired as the destination regions.
  • the system also employs the rendering engine 17 projective alpha channel masking and composition layering to produce the detail of the wrought iron frame.
  • Fig. 46 images 1910, 1920. 1930, and 1940 show several frames of a real-time interactive 3D simulation rendered by the SHOP node.
  • the two cabinets and the pme box are placed at various positions on the floor throughout the 3D room scene.
  • the mirror and artwork products remain attached in place on the walls.
  • image 1910 the mirror is moved on the wall to another location (not visible).
  • Fig. 48 image 1990 is a frame of the output display showing the deployment of PUP2-20 attached to the CD cabinet and PUP3-30 attached to the pine storage box.
  • the shopper clicks on the PUPs to view and hear about the product specifications, pricing, and options.
  • the user clicks on a PUP and the system launches the order transaction system of the attached SELL node.

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Abstract

L'invention concerne un système et des procédés permettant d'accéder à des images numériques 2D et à des modèles géométriques 3D sur un réseau (de préférence, Internet), et à transformer et composer ces supports avec des supports d'images numériques 2D et de modèles géométriques 3D, acquis par d'autres moyens, en représentations d'images 2D et de modèles 3D perfectionnées, permettant la visualisation et la simulation d'une réalité virtuelle. Des images et des modèles numériques d'un réseau et d'autres sources sont incorporées et manipulées par l'intermédiaire d'une interface utilisateur graphique interactive. Un moteur de traitement de support photogrammétrique extrait automatiquement des modèles de capteur virtuel (caméra) et géométriques de l'imagerie. Les données extraites sont utilisées par un processeur de reconstruction, afin de composer de manière automatique et réaliste des images et des modèles. Un système de rendu d'image permet une visualisation et une simulation en temps réel du support construit. L'invention concerne également un modèle de traitement client-serveur pour le déploiement du système moteur de traitement de support sur un réseau.
PCT/US2000/017339 1999-06-25 2000-06-23 Moteur photogrammetrique servant a la construction de modeles WO2001001075A2 (fr)

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Application Number Priority Date Filing Date Title
AU56351/00A AU5635100A (en) 1999-06-25 2000-06-23 Photogrammetry engine for model construction

Applications Claiming Priority (2)

Application Number Priority Date Filing Date Title
US09/344,814 US6912293B1 (en) 1998-06-26 1999-06-25 Photogrammetry engine for model construction
US09/344,814 1999-06-25

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WO2020051208A1 (fr) * 2018-09-04 2020-03-12 Chosid Jessica Procédé d'obtention de données photogrammétriques à l'aide d'une approche en couches
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WO2023199575A1 (fr) * 2022-04-12 2023-10-19 三菱重工業株式会社 Dispositif de génération de modèle, système de génération de modèle, procédé de génération de modèle, et programme

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Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US7046839B1 (en) 2000-09-19 2006-05-16 Spg Hydro International Inc. Techniques for photogrammetric systems
WO2001073688A3 (fr) * 2001-03-28 2002-06-06 Robert Massen Procedes et systeme de traitement de donnees permettant de simplifier le commerce electronique de produits devant etre adaptes a la forme d'un objet
WO2001073688A2 (fr) * 2001-03-28 2001-10-04 Robert Massen Procedes et systeme de traitement de donnees permettant de simplifier le commerce electronique de produits devant etre adaptes a la forme d'un objet
EP1407612A2 (fr) * 2001-05-29 2004-04-14 Koninklijke Philips Electronics N.V. Signal de communication visuelle pour images 3d
US8031190B2 (en) 2004-05-03 2011-10-04 Microsoft Corporation Translating two-dimensional user input on three-dimensional scene
AU2004319589B2 (en) * 2004-05-03 2010-11-25 Microsoft Technology Licensing, Llc Integration of three dimensional scene hierarchy into two dimensional compositing system
AU2010227110B2 (en) * 2004-05-03 2011-12-15 Microsoft Technology Licensing, Llc Integration of three dimensional scene hierarchy into two dimensional compositing system
WO2005124594A1 (fr) * 2004-06-16 2005-12-29 Koninklijke Philips Electronics, N.V. Etiquetage automatique en temps reel de points superposes et d'objets d'interet dans une image visualisee
WO2007014966A1 (fr) * 2005-08-04 2007-02-08 Gesellschaft zur Förderung angewandter Informatik e.V. Procede et dispositif pour determiner la position relative d'un premier objet par rapport a un second objet, programme informatique correspondant, et support d'enregistrement correspondant, lisible par ordinateur
US7711507B2 (en) 2005-08-04 2010-05-04 Gesellschaft Zur Foerderung Angewandter Informatik E.V. Method and device for determining the relative position of a first object with respect to a second object, corresponding computer program and a computer-readable storage medium
EP2505961A3 (fr) * 2011-03-31 2013-04-24 Sensopia, Inc. Appareil, outil et procédé permettant de modifier une partie d'un plan de sol sur la base des mesures effectuées par un ou plusieurs capteurs
US9151608B2 (en) 2011-03-31 2015-10-06 Francis Ruben Malka Apparatus, tool, and method for modifying a portion of a floor plan based on measurements made by one or more sensors
WO2020051208A1 (fr) * 2018-09-04 2020-03-12 Chosid Jessica Procédé d'obtention de données photogrammétriques à l'aide d'une approche en couches
CN109948241A (zh) * 2019-03-15 2019-06-28 西京学院 一种装配式建筑设计装置及方法
CN111399655A (zh) * 2020-03-27 2020-07-10 吴京 一种基于vr同步的图像处理方法及装置
CN111399655B (zh) * 2020-03-27 2024-04-26 吴京 一种基于vr同步的图像处理方法及装置
WO2023199575A1 (fr) * 2022-04-12 2023-10-19 三菱重工業株式会社 Dispositif de génération de modèle, système de génération de modèle, procédé de génération de modèle, et programme

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