CN112734930B - Three-dimensional model light weight method, system, storage medium and image processing device - Google Patents
Three-dimensional model light weight method, system, storage medium and image processing device Download PDFInfo
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Abstract
A three-dimensional model light weight method, system, storage medium and image processing device, the light weight method includes: loading an original model file and reading original model data, wherein the original model data comprises: model mesh, points, face number, and model size; performing front-end grid optimization processing on the original model to generate a front-end optimized grid model with uniform wiring; carrying out surface reduction treatment on the front-end optimized grid model to obtain a surface reduction grid model after the surface reduction treatment; performing UV spreading treatment on the reduced-surface grid model, and splitting model elements and mapping to obtain a mapping-free model and a UV texture set; and baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture atlas. The technical scheme provided by the application can greatly shorten the light-weight processing time of the large model, improve the light-weight ratio of the model and realize that the model is easier to open and edit in three-dimensional software and clients.
Description
Technical Field
The invention relates to a three-dimensional model light-weight method, in particular to a model light-weight method based on a grid topology algorithm, and belongs to the technical field of image processing; the invention also relates to a model light-weight system based on the grid topology algorithm; the invention also relates to a computer readable storage medium; the invention also relates to an image processing device.
Background
The three-dimensional technology is applied to various industries, the model size and model complexity are larger, the model processing difficulty is also increased, and most of model processing adopts manual processing, so that the processing speed is low and the processing capacity for the model is limited. The large model and the complex model are rapidly processed, the model can be reduced as much as possible, the light model can be rapidly opened and edited on various three-dimensional software and clients, and the working convenience of three-dimensional model users can be greatly improved.
Therefore, how to provide a three-dimensional model light-weight method that can advantageously increase the speed of the next stage UV treatment and baking treatment; greatly shortens the light weight processing time of the large model, improves the light weight ratio of the model, realizes that the model is easier to open and edit in three-dimensional software and clients, and is a technical problem to be solved urgently by those skilled in the art.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to solve the problem of opening and editing a large model in various three-dimensional software and clients, and is based on the whole model processing flow, the original model is subjected to re-gridding to generate a topological model with uniform vertexes, an optimization method is provided to improve the processing speed of stages such as UV spreading and baking, the light-weight processing time of the large model is greatly shortened, the light-weight ratio of the model is improved, and the model is easier to open and edit in the three-dimensional software and clients. The invention provides a model light weight method based on a grid topology algorithm, which comprises the following steps: loading an original model file and reading original model data, wherein the original model data comprises: model mesh, points, face number, and model size; performing front-end grid optimization processing on the original model to generate a front-end optimized grid model with uniform wiring; carrying out surface reduction treatment on the front-end optimized grid model to obtain a surface reduction grid model after the surface reduction treatment; performing UV spreading treatment on the reduced-surface grid model, and splitting model elements and mapping to obtain a mapping-free model and a UV texture set; and baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture atlas.
According to a first embodiment of the present invention, there is provided a model light-weight method based on a mesh topology algorithm:
a model lightening method based on a mesh topology algorithm, the lightening method comprising:
Loading an original model file and reading original model data, wherein the original model data comprises: model mesh, points, face number, and model size;
performing front-end grid optimization processing on the original model to generate a front-end optimized grid model with uniform wiring;
carrying out surface reduction treatment on the front-end optimized grid model to obtain a surface reduction grid model after the surface reduction treatment;
Performing UV spreading treatment on the reduced-surface grid model, and splitting model elements and mapping to obtain a mapping-free model and a UV texture set;
And baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture atlas.
Further, as a more preferred embodiment of the present invention, the pre-grid optimization process includes the steps of:
Performing basic optimization on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped part models to obtain a basic optimization model;
And carrying out grid normalization processing on the basic optimization model, and constructing uniform smooth grids on the basic optimization model to obtain a pre-optimized grid model.
It should be noted that:
1) Model file: the 3D model (original model) describes a stereoscopic space in a file
2) The grid topology is to lay out, structure and connection the dot-line surface of the polygonal grid model. If a model has a good topological structure, the appearance of wiring of the model is clean and regular, the processing efficiency of the model can be improved to a great extent, the light weight speed is increased, and the light weight capability is improved.
3) The UV spreading treatment is a process of reasonably tiling the UV texture surface of the original model on a two-dimensional canvas (UV texture set) to reasonably distribute the UV texture surface.
4) The baking (bak) process is a process of saving geometric features of a 3D mesh to a texture file (bitmap file). The characteristics (including texture, and illumination) of various combinations are baked from 3D object properties (ambient light occlusion, normal, vertex color, direction, curvature, position, etc.) into a single texture (texture atlas), which in turn can be re-mapped to the model object using the UV coordinates of the object.
Further, as a more preferable embodiment of the present invention, the "building a uniform smooth mesh on the basic optimization model" is specifically: and constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm.
Further, as a more preferred embodiment of the present invention, the mesh topology algorithm comprises the steps of:
Reading the basic optimization model, constructing a defined neighborhood relation of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
Constructing an optimized direction field and a position field;
performing grid extraction processing, converting the calculated fields into grids, and obtaining a grid structure;
Outputting the pre-optimized grid model, wherein the pre-optimized grid model is divided into: the method comprises the steps of reserving a prepositive optimized grid model of a structure and not reserving the prepositive optimized grid model of the structure.
Further, as a more preferred embodiment of the present invention, the method for outputting the pre-optimized mesh model of the reserved structure is as follows: and carrying out topological optimization on all grid objects in the file according to a pre-recorded model structure of the original model, and carrying out file restoration according to the model structure to generate a pre-optimized grid model with a reserved structure.
Further, as a more preferable embodiment of the present invention, the method for outputting the pre-optimized mesh model without retaining the structure is as follows: and merging all grid objects of the original model, performing topology optimization, and directly generating a pre-optimized grid model without retaining a structure.
Further, as a more preferable embodiment of the present invention, the light weight method further comprises:
reading and converting the non-chartlet model into a target model file according to a target file format;
reading and converting the texture map set into a target map file according to a target file format;
and storing the target model file and the target map file into a memory.
According to a second embodiment of the present invention, there is provided a model lightweight system based on a mesh topology algorithm:
a model lightweight system based on a mesh topology algorithm, the system comprising:
The loading and reading device is used for loading the original model file and reading the original model data;
The front grid optimization processing device is used for carrying out front grid optimization processing on the original model and generating a front optimization grid model with uniform wiring;
The surface reduction processing device is used for carrying out surface reduction processing on the preposed optimized grid model and obtaining a surface reduction grid model after the surface reduction processing;
The UV spreading processing device is used for performing UV spreading processing on the reduced-surface grid model, splitting model elements and the chartlet into elements, and obtaining a chartlet-free model and a UV texture set;
and the baking processing device is used for baking the UV texture set, mapping the color of the face-reduced grid model onto the UV texture set, and obtaining a texture atlas.
According to a third embodiment of the present invention, there is provided a computer-readable storage medium:
a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method according to the first embodiment.
According to a fourth embodiment of the present invention, there is provided an image processing apparatus:
An image processing apparatus, the terminal device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described in the first embodiment when the computer program is executed.
Compared with the prior art, the technical scheme of the application has the following technical effects:
1. according to the technical scheme provided by the application, the UV utilization rate is improved, the optimal effect of the model can be represented on the premise of limiting the mapping accuracy specification, mapping resources can be saved, and the operation of the method on terminal equipment is smoother.
Drawings
FIG. 1 is a flow chart of a model lightweight method based on a mesh topology algorithm in an embodiment of the invention;
fig. 2 is a schematic diagram of a connection structure of a model lightweight system based on a mesh topology algorithm in an embodiment of the present invention;
FIG. 3 is a schematic diagram of constructing an optimized direction field in an embodiment of the invention;
FIG. 4 is a schematic diagram of constructing an optimized location field in an embodiment of the invention;
FIG. 5 is a diagram illustrating grid normalization in accordance with an embodiment of the present invention;
FIG. 6 is a schematic view showing the angles of the connected triangular surfaces according to an embodiment of the present invention
Figure 7 is a diagram of an original model grid that is not meshed in an embodiment of the invention,
Dividing lines of the original model mesh that are not gridded: s1, a first to-be-unfolded surface block of an original model grid which is not meshed is as follows: c1, a second to-be-developed surface block of the original model grid which is not meshed: c2;
Figure 8 is a diagram of a grid of a topological model after meshing in an embodiment of the present invention,
Dividing lines of the meshed topological model mesh: s2, a first to-be-unfolded surface block of the topological model grid after gridding: d1, a second to-be-developed surface block of the topological model grid after gridding: d2;
Figure 9 is a UV plan view of an unsingulated original model after UV development in an embodiment of the present invention,
UV block after UV-spreading of the raw model without gridding: WGZ1, non-gridded raw model post-UV void: KX1, an un-meshed first UV block C1 'after UV is displayed on a first to-be-displayed surface block C1 of the un-meshed original model grid, and an un-meshed second UV block C2' after UV is displayed on a second to-be-displayed surface block C2 of the un-meshed original model grid;
FIG. 10 is an enlarged aggregate view of all of the UV blocks of FIG. 9, with the gaps between the UV blocks reduced;
FIG. 11 is a UV plan view of a rasterized topology model after UV development in accordance with an embodiment of the present invention,
UV block after UV-spreading of the meshed topology model: WGZ2, the topology model after gridding is empty after UV spreading: KX2, the first UV block D1' after UV expansion of the first to-be-expanded face block D1 of the topological model grid after gridding,
The second UV block D2' after UV is developed by the second to-be-developed surface block D2 of the topological model grid after gridding;
FIG. 12 is a schematic diagram of grid normalization in the prior art;
FIG. 13 is a diagram illustrating grid normalization in accordance with an embodiment of the present invention;
fig. 14 is a Mesh enlarged area grid chart in an embodiment of the present invention;
fig. 15 is a simplified diagram of Mesh-enlarged area grid in an embodiment of the present invention.
Detailed Description
In order that those skilled in the art will better understand the technical solutions of the present application, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
It will be understood that when an element is referred to as being "fixed" or "disposed" on another element, it can be directly on the other element or be indirectly on the other element; when an element is referred to as being "connected to" another element, it can be directly connected to the other element or be indirectly connected to the other element.
It is to be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like are merely for convenience in describing and simplifying the description based on the orientation or positional relationship shown in the drawings, and do not indicate or imply that the devices or elements referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus are not to be construed as limiting the application.
Furthermore, the terms "first," "second," and the like, are used for descriptive purposes only and are not to be construed as indicating or implying a relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defining "a first" or "a second" may explicitly or implicitly include one or more such feature. In the description of the present application, the meaning of "a plurality" or "a number" means two or more, unless specifically defined otherwise.
It should be understood that the structures, proportions, sizes, etc. shown in the drawings are for the purpose of understanding and reading the disclosure, and are not intended to limit the scope of the application, which is defined by the claims, but rather by the claims, unless otherwise indicated, and that any structural modifications, proportional changes, or dimensional adjustments, which would otherwise be apparent to those skilled in the art, would be made without departing from the spirit and scope of the application.
According to a first embodiment of the present invention, there is provided a model light-weight method based on a mesh topology algorithm:
A model lightening method based on a mesh topology algorithm, the lightening method comprising: loading an original model file and reading original model data, wherein the original model data comprises: model mesh, points, face number, and model size;
performing front-end grid optimization processing on the original model to generate a front-end optimized grid model with uniform wiring;
carrying out surface reduction treatment on the front-end optimized grid model to obtain a surface reduction grid model after the surface reduction treatment;
Performing UV spreading treatment on the reduced-surface grid model, and splitting model elements and mapping to obtain a mapping-free model and a UV texture set;
And baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture atlas.
The application provides a model light-weight method based on a grid topology algorithm. According to the method, after an original model is loaded and original model data are read, pre-grid optimization processing is carried out on the original model to generate a pre-optimized grid model with uniform wiring, and then face reduction processing is carried out on the pre-optimized grid model to obtain a face reduction grid model after face reduction processing; in the process of optimizing the prepositive grid, a prepositive optimized grid model with uniform wiring is generated, and the uniform grid model can enable the surface material of the model to rapidly project a map along the discovery vertical to the surface in the process of UV spreading, so that the technical scheme provided by the application can greatly improve the speeds of the next stage UV spreading treatment and baking treatment; greatly shortens the light weight processing time of the large model, improves the light weight ratio of the model, and realizes that the model is easier to open and edit in three-dimensional software and clients.
It should be noted that "UV" refers herein to the abbreviation of u, v texture map coordinates (which are similar to the X, Y, Z axes of a spatial model). It defines information of the position of each point on the picture. The UV is the process of interpolating the image smoothly by software at the point-to-point gap locations.
It should be noted that, the model general processing method is as follows: the model process flow includes subtractive surface, spread UV and baking. Since model pre-optimization generates a topology model with unified rules, nodes in a processing flow, such as single-node processing (face reduction), such as multiple node combination processing (UV spreading and baking), can be flexibly selected.
It should be noted that, the UV spreading treatment actually improves the mapping accuracy of the limited size, so that the model is perfectly matched with the mapping; the method is an idea provided for reducing the waste of resources, changes the previous one-to-one relationship into one-to-many relationship, reduces the repeated UV part, greatly improves the utilization rate of UV and reduces the resources occupied by mapping.
Specifically, in the embodiment of the invention, the surface reduction (surface reduction processing) is performed by the model according to the surface reduction parameters according to the read surface reduction parameters.
Specifically, in the embodiment of the present invention, UV (UV-spreading treatment): and developing the UV texture on the model, reading the model and tiling the UV maps in all the Mesh in the model on the UV texture.
Specifically, in the embodiment of the present invention, baking (baking treatment): mapping the colors of the high mode and the low mode of the model to UV, baking the model texture after UV spreading to generate a corresponding texture map, and then giving the map to the model. Two ways of baking are provided: the method comprises the steps that a first mode and a traditional mode are used for rendering basic colors, normals and illumination onto model textures, and color mapping and normals mapping are generated; the second, sub-age mode can be PBR bake and high light bake. Generating a color map, a normal map, an AO map, a metallicity map and a roughness map by PBR baking energy; the highlight baking can generate color maps, normal maps, AO maps, and highlight maps.
Specifically describing, in an embodiment of the present invention, the pre-grid optimization process includes the following steps: performing basic optimization on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped part models to obtain a basic optimization model; and carrying out grid normalization processing on the basic optimization model, and constructing uniform smooth grids on the basic optimization model to obtain a pre-optimized grid model.
The basic optimization process is to delete repeated vertices of the model, repair joints of the model and remove the model with overlapped parts, so that the original model is internally fused into a whole (original contour whole) under the condition of keeping the appearance structure unchanged, and the original contour whole is reduced in a large amount of repeated vertices, lines and overlapping entity data compared with the original model on basic data. For example: the original model is a cup body and a cup cover of the vacuum cup; the thermos cup presents a state that the cup body is covered on the cup cover, and then the threaded structure of the connecting part of the cup body and the cup cover is the overlapping part, which is redundant vertex or line. The original model of the vacuum cup is optimized to be the whole data of the original outline with the appearance (shell) data of the vacuum cup through basic optimization processing, so that the data structure of the original model is greatly optimized, the data volume is reduced, the difficulty in gridding the original model in the later stage is reduced, and the speeds of gridding processing, UV spreading processing and baking processing in the later stage are improved.
The grid normalization process is to construct a uniform and smooth grid through a grid topology algorithm; the 'construction of a uniform and smooth grid' is a triangular mesh. In the prior art, in order to improve the smoothness of the model body after gridding, as shown in fig. 5, the surface A1 and the surface B2 are formed by a four-corner surface, but the four-corner surface is folded in half in space according to a dotted line Y, and the four-corner surface is not smooth. When the newly added line Y splits the four corner faces into 2 triangular faces, faces A1 and B2 are generated, and these 2 faces are smooth. Different numbers of angular surfaces are used for meshing the contours of different entities, as shown in fig. 12-12: triangular face J3, square face J4, pentagonal face J5, etc. In the application scene of the scheme of the application, the model file processed by the model light weight method based on the grid topology algorithm is mainly used for displaying on a small screen terminal. In the process of showing the model file processed by the scheme of the application by the small-screen terminal, a certain distance is reserved between the small-screen terminal and human eyes, and the content of the model file displayed at the same time has continuous displacement action in a display area, so that the use scene of the scheme of the application determines that the precision requirement on the model file which needs to be loaded after processing optimization is not high. Therefore, in the scheme of the application, the basic optimization model is optimized into the front optimization grid model which is all triangular surface grids, so that UV spreading treatment is convenient to carry out later.
Further, after the grids are unified into the triangular faces, the system does not need to judge grid angle face processing when the subsequent operation is carried out, namely judging whether the grids are triangular faces or four-angle faces at present; meanwhile, the system does not need to call complex functions to process four-angle surfaces or five-angle surfaces; the method greatly accelerates the overall processing speed, optimizes the data flow in the process of executing the method provided by the application by the original model file, and promotes the development of subsequent processing.
Note that, the pre-grid optimization process: mainly comprises two steps, namely, the first step of model basic optimization and the second step of grid normalization. Normalizing the optimization model is beneficial to the following process, and during the process of developing UV, we encounter a lot of problems, such as very chaotic Mesh wiring, very slow during the process of automatically developing UV, and think that if one model has 30 faces we need to calculate 30 ten thousand times during the process of processing the process of developing UV. And the normalized optimization later optimizes the face number to 10 vans. Only 10 ten thousand calculations are needed during the flattening process. The number of computations is reduced after normalization. Also, for example, wiring is not uniform during calculation, many scattered surfaces may be cut out when the cut surfaces are flattened, and space occupation ratio for UV becomes low. The UV calculation process is also slow and time consuming due to flattening many fragmented surfaces. If the UV expansion occupancy is higher by the uniform wiring after normalization, the generation of the later baking map, AO map and highlight map is faster, and the map quality is finer.
It should be noted that, the basic optimization process specifically includes: deleting repeated vertexes in the original model grid, repairing joints and removing duplication of the grid; the basic optimization process further comprises: repairing the abnormal model.
Preferably, in an embodiment of the present invention, the UV spreading treatment comprises the steps of:
counting grid data information of the face-reduced grid model;
and tiling the surface grids of the face-reduced grid model on a two-dimensional canvas in a tiling unfolding mode to obtain the UV texture set.
As shown in fig. 6, the mesh formed after the mesh normalization is a triangular mesh; from a microscopic perspective, only 3 sides of a1 triangular mesh are connected to the surrounding mesh. Therefore, when the relationship between the triangular surface and any other triangular surface is processed, the actual area after flattening can be calculated only by calculating the included angle between the two surfaces (such as the included angle alpha formed by the triangular surface A and the triangular surface B). Compared with grids with four corners, five corners and the like being larger than the triangular surface, the grid is more beneficial to the processing of a terminal processor. Namely, the step of carrying out the front grid optimization treatment on the original model in the application scheme of the application is greatly beneficial to the step of carrying out the face reduction treatment on the front grid optimization model; has great promoting effect.
Specifically describing, in the embodiment of the present invention, the "building a uniform smooth grid on the basic optimization model" is specifically: and constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm.
It should be noted that, constructing a uniform and smooth grid can facilitate cutting the grid when UV is being developed. If the original model is directly subjected to UV unfolding after being loaded, the original model contains excessive repeated point, line and surface data, so that the following problems are caused relative to the scheme of the application: first, as can be seen from comparison of fig. 7 and fig. 8, the area of a single grid is small, the number of grids is large, and a great deal of calculation is needed in the UV spreading process; second, as can be seen from a comparison of fig. 9 and 11, due to the complicated structure, a large number of UV blocks are separated in the UV spreading process, and each UV block includes a plurality of single grids, which reduces the efficiency of baking when the baking operation is performed later; thirdly, as can be seen from comparison between fig. 9 and fig. 11, in the baking process, the UV texture sets need to be addressed, and the addressing time is too long due to the large void ratio between each UV block, so that the baking process is inefficient; fourth, as can be seen from comparison between fig. 9 and fig. 11, the area of the UV texture set in the prior art is large due to the large void ratio between each UV block, the usage rate of the UV texture set is not high, the operation pressure is large, and the storage pressure is large; fifth, as can be seen from comparison of fig. 9 and fig. 11, on the premise of UV texture sets with the same resolution, the definition of a single grid in the prior art is lower than that of the scheme of the present application due to the large void ratio between each UV block.
Based on the reasons, the scheme of the application carries out the pre-grid optimization processing on the original model data to generate the pre-optimized grid model with uniform wiring, which is greatly beneficial to the UV spreading processing and baking carding of the grid data in the later period, improves the overall processing speed and improves the processing quality.
It should be further noted that, from the two Mesh objects of fig. 7 and 8, the wiring S2 and the number of faces of fig. 8 are significantly better than those of fig. 7, and the wiring S1 of fig. 7 is disordered and complex. Fig. 8 shows Mesh objects obtained by normalization optimization, dark black lines are calculated UV-spreading parting lines S2, and the program will flatten the model with reference to the parting lines. In fig. 8, the number of faces of the model is optimized to repair some errors (continuity, orphan, broken faces, etc.) possibly existing in the original Mesh, and a new Mesh object is obtained after the Mesh is reconstructed. The general UV map determines the usage rate of the post-baking map, fig. 9 is a UV map obtained after the original model exhibits UV, fig. 11 is a normalized UV map, and it can be seen from the figure that the usage rate of pixels in the picture of 1920 x 1920 in fig. 11 is significantly higher than that in fig. 9, that is, the clarity of the picture produced after baking in fig. 11 is higher than that in fig. 9. The UV utilization of fig. 11 is also significantly higher than that of fig. 9. Whether the wiring of the model is even or not is related to whether the UV expansion is flat or not, and the reasonable utilization of the space of the UV is related to the drawing of the map.
Specifically describing, in an embodiment of the present invention, the mesh topology algorithm includes the following steps:
Reading the basic optimization model, constructing a defined neighborhood relation of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
Constructing an optimized direction field and a position field;
performing grid extraction processing, converting the calculated fields into grids, and obtaining a grid structure;
Outputting the pre-optimized grid model, wherein the pre-optimized grid model is divided into: the method comprises the steps of reserving a prepositive optimized grid model of a structure and not reserving the prepositive optimized grid model of the structure.
Specifically describing, in the embodiment of the present invention, the method for outputting the pre-optimized mesh model of the reserved structure is as follows: and carrying out topological optimization on all grid objects in the file according to a pre-recorded model structure of the original model, and carrying out file restoration according to the model structure to generate a pre-optimized grid model with a reserved structure.
Specifically describing, in the embodiment of the present invention, the method for outputting the pre-optimized mesh model without retaining the structure is: and merging all grid objects of the original model, performing topology optimization, and directly generating a pre-optimized grid model without retaining a structure.
Specifically describing, in an embodiment of the present invention, the light weight method further includes:
reading and converting the non-chartlet model into a target model file according to a target file format;
reading and converting the texture map set into a target map file according to a target file format;
and storing the target model file and the target map file into a memory.
Note that, output: the system supports multiple output forms, and supports different forms of model output and 360-degree picture output. Wherein, first, format (object model file) output: reading an externally input target format, reading a topology model, and performing format conversion on the baked topology model according to the target format to generate a target model (comprising a target map file); second, 360 ° picture (model picture generated from different angles rendering): reading the topology model, reading the number of externally input pictures, rendering the baked topology model according to 360 degrees according to the number of the pictures, and generating corresponding numbers of pictures.
According to a second embodiment of the present invention, there is provided a model lightweight system based on a mesh topology algorithm:
a model lightweight system based on a mesh topology algorithm, the system comprising:
The loading and reading device is used for loading the original model file and reading the original model data;
The front grid optimization processing device is used for carrying out front grid optimization processing on the original model and generating a front optimization grid model with uniform wiring;
The surface reduction processing device is used for carrying out surface reduction processing on the preposed optimized grid model and obtaining a surface reduction grid model after the surface reduction processing;
The UV spreading processing device is used for performing UV spreading processing on the reduced-surface grid model, splitting model elements and the chartlet into elements, and obtaining a chartlet-free model and a UV texture set;
and the baking processing device is used for baking the UV texture set, mapping the color of the face-reduced grid model onto the UV texture set, and obtaining a texture atlas.
According to a third embodiment of the present invention, there is provided a computer-readable storage medium:
a computer readable storage medium storing a computer program which, when executed by a processor, performs the steps of the method according to the first embodiment.
According to a fourth embodiment of the present invention, there is provided an image processing apparatus:
An image processing apparatus, the terminal device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described in the first embodiment when the computer program is executed.
It should be noted that the present invention aims to provide a new method for compressing and optimizing a model, which can further compress three-dimensional model data and increase the speed of network transmission and rendering speed. The technical scheme adopted by the invention is as follows: firstly, carrying out model pre-optimization on an original model on the basis of retaining the shape, reconstructing a gridding model, and then executing a general model processing flow on the gridding model: and (5) reducing the surface, spreading UV and baking to finally realize the light weight treatment of the model.
Model base optimization (base optimization process): deleting repeated vertexes in the Mesh, repairing joints and deduplication of the Mesh, repairing abnormal models (such as abnormal materials, abnormal isolated point Mesh and the like),
Grid normalization (grid normalization process): mesh repartitioning an isotropic surface using a unified locally smoothed triangle or quadrilateral main Mesh to optimize the operators of edge direction and vertex position in the output Mesh, and finally converting the original digitized polygon Mesh into a clean normalized Mesh. The scheme mainly uses a grid topology algorithm, the topology algorithm combines the ideas of local and global gridding methods, a local direction and position field smoothing algorithm is used for calculating grids globally aligned with a direction field, then the grids are extracted from fields, and post-processing is carried out.
The grid topology algorithm mainly comprises the following steps: model reading: we store the input surface model with a set of edges in which each vertex is associated with a location and a normal, and define the neighborhood relationship in various ways based on the input: each pair of adjacent vertices has an associated weight. The weights may be chosen to be uniform or related to several to better accommodate irregular inputs.
Directional field optimization (construction optimized direction field): the orientation field is calculated because the word can know the alignment of the edges in the final mesh. The directional field satisfies the degree of rotational symmetry condition, which means that each vertex is associated with a set of tangent vectors of uniform spacing, the values associated with which are reduced to conventional tangent vector fields.
Wherein, as shown in FIG. 3, the first formula is to rotate o around the normal vector nThe second expression is to rotate n times with o symmetry at the vertex to form an n-Rosy direction field.
Location field optimization (build optimized location field): given the orientation RoSy field O (calculated using our method or other field design algorithm), we now calculate a local parameterization whose gradient is aligned with O, as in fig. 4. The global parameterization algorithm calculates single consistent parameterization, and the gradient of the single consistent parameterization is matched with the direction field in the least square sense;
Two graphical representations of position field smoothness energy: in the inherent case, all vj related quantities are rotated into the tangent plane of vi; thereafter, the representative position closest to pi is determined. In the external case, the rotation will be omitted and both positions will translate. The final representative position is drawn in dark.
And (3) outputting: the method comprises the steps of dividing a reserved structure and an unreserved structure, and judging and generating a reserved structure or an unreserved structure model according to external input parameters.
It should be noted that, as the UV calculation is performed according to the original model of fig. 7 to generate the cut lines, the Mesh object of fig. 7 is clearly divided into the UV blocks in fig. 9 by the spanuv software as seen from the cut lines represented by the darkened black lines.
Using the same spanuv software as in fig. 7, the UV calculation was performed on fig. 8 to generate cut lines, and it is apparent from the cut lines represented by the darkened black lines that the Mesh objects in fig. 8 were divided into only 4 Mesh objects.
The range of the bold lines in fig. 14 is the Mesh-enlarged area, and after the Mesh topology, the Mesh of the range of the bold lines in fig. 14 is simplified to form fig. 15, and then the UV map with the difference in the number of UV blocks in fig. 9 and 11 is formed by the same UV spreading algorithm.
Example 1
A model lightening method based on a mesh topology algorithm, the lightening method comprising:
Loading an original model file and reading original model data, wherein the original model data comprises: model mesh, points, face number, and model size;
performing front-end grid optimization processing on the original model to generate a front-end optimized grid model with uniform wiring;
carrying out surface reduction treatment on the front-end optimized grid model to obtain a surface reduction grid model after the surface reduction treatment;
Performing UV spreading treatment on the reduced-surface grid model, and splitting model elements and mapping to obtain a mapping-free model and a UV texture set;
And baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture atlas.
Example 2
Example 1 is repeated except that the pre-grid optimization process includes the steps of:
Performing basic optimization on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped part models to obtain a basic optimization model;
And carrying out grid normalization processing on the basic optimization model, and constructing uniform smooth grids on the basic optimization model to obtain a pre-optimized grid model.
Example 3
Example 2 is repeated except that the "build uniform smooth mesh on the base optimization model" is specifically: and constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm.
Example 4
Example 3 is repeated except that the mesh topology algorithm comprises the steps of:
Reading the basic optimization model, constructing a defined neighborhood relation of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
Constructing an optimized direction field and a position field;
performing grid extraction processing, converting the calculated fields into grids, and obtaining a grid structure;
Outputting the pre-optimized grid model, wherein the pre-optimized grid model is divided into: the method comprises the steps of reserving a prepositive optimized grid model of a structure and not reserving the prepositive optimized grid model of the structure.
Example 5
Example 4 is repeated except that the method of outputting the pre-optimized mesh model of the retention structure is: and carrying out topological optimization on all grid objects in the file according to a pre-recorded model structure of the original model, and carrying out file restoration according to the model structure to generate a pre-optimized grid model with a reserved structure.
Example 6
Example 5 is repeated except that the method of outputting the pre-optimized mesh model without retaining the structure is: and merging all grid objects of the original model, performing topology optimization, and directly generating a pre-optimized grid model without retaining a structure.
Example 7
Example 1 was repeated except that the light weight method further comprises:
reading and converting the non-chartlet model into a target model file according to a target file format;
reading and converting the texture map set into a target map file according to a target file format;
and storing the target model file and the target map file into a memory.
Example 8
A model lightweight system based on a mesh topology algorithm, the system comprising:
The loading and reading device is used for loading the original model file and reading the original model data;
The front grid optimization processing device is used for carrying out front grid optimization processing on the original model and generating a front optimization grid model with uniform wiring;
The surface reduction processing device is used for carrying out surface reduction processing on the preposed optimized grid model and obtaining a surface reduction grid model after the surface reduction processing;
The UV spreading processing device is used for performing UV spreading processing on the reduced-surface grid model, splitting model elements and the chartlet into elements, and obtaining a chartlet-free model and a UV texture set;
and the baking processing device is used for baking the UV texture set, mapping the color of the face-reduced grid model onto the UV texture set, and obtaining a texture atlas.
Example 10
A computer readable storage medium storing a computer program which, when executed by a processor, implements the steps of the method of embodiment 6.
According to a fourth embodiment of the present invention, there is provided an image processing apparatus:
An image processing apparatus, the terminal device comprising: a memory, a processor and a computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as described in embodiment 6 when the computer program is executed.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (7)
1. The model light weight method based on the grid topology algorithm is characterized by comprising the following steps of:
Loading an original model file and reading original model data, wherein the original model data comprises: model mesh, points, face number, and model size; performing front-end grid optimization processing on the original model to generate a front-end optimized grid model with uniform wiring; carrying out surface reduction treatment on the front-end optimized grid model to obtain a surface reduction grid model after the surface reduction treatment; performing UV spreading treatment on the reduced-surface grid model, and splitting model elements and map elements to obtain a non-map model and a UV texture set; baking the UV texture set, and mapping the color of the face-reduced grid model to the UV texture set to obtain a texture atlas; the pre-grid optimization process comprises the following steps: performing basic optimization on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped part models to obtain a basic optimization model; grid normalization processing is carried out on the basic optimization model, and a uniform smooth grid is constructed on the basic optimization model to obtain a pre-optimized grid model; the "constructing a uniform smooth grid on the basic optimization model" specifically includes: constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm; the grid topology algorithm comprises the following steps: reading the basic optimization model, constructing a defined neighborhood relation of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes; constructing an optimized direction field and a position field; performing grid extraction processing, converting the calculated fields into grids, and obtaining a grid structure; outputting the pre-optimized grid model, wherein the pre-optimized grid model is divided into: the method comprises the steps of reserving a prepositive optimized grid model of a structure and not reserving the prepositive optimized grid model of the structure.
2. The method for model weight reduction based on a mesh topology algorithm according to claim 1, wherein the method for outputting the pre-optimized mesh model of the reserved structure is as follows: and carrying out topological optimization on all grid objects in the file according to a pre-recorded model structure of the original model, and carrying out file restoration according to the model structure to generate a pre-optimized grid model with a reserved structure.
3. The method for model weight reduction based on the grid topology algorithm according to claim 2, wherein the method for outputting the pre-optimized grid model without retaining the structure is as follows: and merging all grid objects of the original model, performing topology optimization, and directly generating a pre-optimized grid model without retaining a structure.
4. A method of model lightening based on a mesh topology algorithm as claimed in any one of claims 1 to 3, further comprising: reading and converting the non-chartlet model into a target model file according to a target file format; reading and converting the texture map set into a target map file according to a target file format; and storing the target model file and the target map file into a memory.
5. A model lightweight system based on a mesh topology algorithm, the system comprising:
The loading and reading device is used for loading the original model file and reading the original model data;
The front grid optimization processing device is used for carrying out front grid optimization processing on the original model and generating a front optimization grid model with uniform wiring; the surface reduction processing device is used for carrying out surface reduction processing on the preposed optimized grid model and obtaining a surface reduction grid model after the surface reduction processing; the UV spreading processing device is used for performing UV spreading processing on the reduced-surface grid model, and splitting model elements and map elements to obtain a non-map model and a UV texture set;
The baking processing device is used for baking the UV texture set, mapping the color of the face-reduced grid model onto the UV texture set, and obtaining a texture atlas; the pre-grid optimization process comprises the following steps: performing basic optimization on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped part models to obtain a basic optimization model; grid normalization processing is carried out on the basic optimization model, and a uniform smooth grid is constructed on the basic optimization model to obtain a pre-optimized grid model; "building a uniform smoothing grid on the base optimization model" includes: constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm; the grid topology algorithm comprises: reading the basic optimization model, constructing a defined neighborhood relation of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes; constructing an optimized direction field and a position field; performing grid extraction processing, converting the calculated fields into grids, and obtaining a grid structure; outputting the pre-optimized grid model, wherein the pre-optimized grid model is divided into: the method comprises the steps of reserving a prepositive optimized grid model of a structure and not reserving the prepositive optimized grid model of the structure.
6. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when executed by a processor, implements the steps of the method as claimed in claims 1-4.
7. An image processing apparatus, comprising: memory, a processor and a computer program stored in the memory and executable on the processor, which processor, when executing the computer program, implements the steps of the method as claimed in claims 1-4.
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