CN111640174A - Furniture growth animation cloud rendering method and system based on fixed visual angle - Google Patents
Furniture growth animation cloud rendering method and system based on fixed visual angle Download PDFInfo
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Abstract
The invention discloses a furniture growth animation cloud rendering method and system based on a fixed visual angle. The method ensures that an operator only needs to provide a rendering scene and a fixed visual angle, the pre-calculation-planning module can automatically calculate the filling sequence of elements in the scene, and construct a frame sequence from an empty scene to a complete scene, the whole construction process is less than 20 seconds, and no manual intervention is needed; the rendering-generating-synthesizing module can automatically generate each frame of image and synthesized video according to the frame sequence, and the whole process only needs a plurality of minutes; a plurality of links needing manual intervention in the prior art can be finished in an automatic mode, and the cost in all aspects is obviously reduced; the method performs targeted optimization on the field of indoor decoration, so that the method has higher application value.
Description
Technical Field
The invention relates to the field of digital media, in particular to a furniture growth animation cloud rendering method and system based on a fixed visual angle.
Background
Growing animation, namely building time-series animation (construction-sequential animation), is an expression form of architectural animation (architectural animation); in a short time, the method gradually fills each element into a space modeling rendering scene by using special effect means such as appearance, displacement, scaling and the like to form a continuous image, and is a digital streaming media work obtained by simplifying and processing the art in the construction process of an objective world.
In the prior art, the production of a growing animation requires the operator to: completing scene modeling in three-dimensional animation software; planning the filling sequence of the elements, and editing the animation presentation of various elements one by using software functions or third-party plug-ins supported by the software; generating a static frame image sequence by combining an offline renderer; and importing the video into nonlinear video editing software to complete the editing.
The prior art has the following disadvantages: an operator is required to master the use of a plurality of software, which are mainly large commercial software, and the software cost and the learning cost are high; operators are required to be equipped with computers higher than the mainstream configuration of the consumer market, such as professional graphic workstations, so that the requirements of the modeling, rendering and video generation on the operating environment can be met, and the hardware cost is high; operators are required to edit and store in a single computer environment meeting the requirements of software and hardware, and the migration cost and risk cost which are extremely dependent on the single computer environment are high; the operator is required to put a great deal of effort into the detailed implementation of the specific animation effect from the macroscopic sequence planning to the microscopic, and the time cost and the labor cost are high. How to overcome the limitations of the prior art and reduce the cost of growing animation from multiple aspects is a problem to be solved urgently.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides a furniture growth animation cloud rendering method and system based on a fixed visual angle.
The purpose of the invention is achieved by the following technical scheme: the furniture growth animation cloud rendering method based on the fixed visual angle mainly comprises the following steps:
1) scene data preprocessing: acquiring the depth, mesh and other meta-information of a rendering object under the current camera view angle, and coding and storing the meta-information into two-dimensional data and necessary auxiliary indexes according to a pre-designed data format;
2) calculating the spatial position relation: deducing and establishing a spatial position relationship of the rendering object by utilizing the result data preprocessed in the step 1, deducing and reconstructing a three-dimensional spatial position relationship of the rendering object from the two-dimensional meta-information, and providing context for automatically planning a filling sequence;
3) planning a growth sequence: automatically planning the filling sequence of each element in the current camera view angle on the basis of the spatial position relation by combining the category marking information and other extended information of the rendering object;
4) generating a key frame image sequence: splitting rendering scene data into a key frame data sequence based on the filling sequence and other user input, and generating a key frame image sequence by combining an offline rendering device;
5) synthesizing an animation video: and filling the frame sequence through the frame interpolation to synthesize the final animation video.
The scene data preprocessing adopts a pre-rendering method, three-dimensional space information in a current camera view angle is extracted into two-dimensional meta-information of a plurality of characteristic spaces, the pre-rendering method stores a mapping relation between a mesh index and a rendering object as a hash through a ray tracing or rasterization rendering algorithm, the hash is used as a calculated auxiliary index, and attribute data of the rendering object contains a unique id.
The calculation of the spatial position relationship mainly comprises the following steps:
1) mapping the mesh meta information to depth information by using an SIMD vectorization method and a hash index, and further establishing mapping of each rendering object and a depth value matrix;
2) the space of the current camera view angle is divided into a plurality of connected subspaces with proper granularity, the rendered objects are clustered according to the depth distance division of the subspaces, the rendered objects are sequenced along the direction of a set spatial axis by using the depth distance in each subspace, and finally the sequencing results in the plurality of subspaces are merged into the spatial position relation of the rendered objects of the current scene.
The growing order plan includes predefined category priority filling order rules, a desired filling order given by the user, and potential composition relationships between elements.
The method comprises the steps of generating a key frame image sequence and a composite animation video, splitting scene data into a key frame data sequence based on the key frame sequence, further generating the key frame image sequence by using an offline rendering device, taking the key frame image sequence as input, generating an interpolation frame by using an image processing method, finally exporting the interpolation frame in a streaming media file format, and delivering a user finished product by relying on public cloud distributed object storage service.
The furniture growth animation cloud rendering system based on the fixed visual angle mainly comprises a scene data preprocessing unit, a spatial position calculating unit, a growth sequence planning and planning unit, a key frame image sequence generating unit and an animation video synthesizing unit, wherein the scene data preprocessing unit acquires the depth, mesh and other meta-information of a rendering object under the current camera visual angle; the spatial position calculation unit deduces the result data of the scene data preprocessing unit to establish the spatial position relationship of the rendering object; the growth sequence planning and planning unit automatically plans the filling sequence of each element in the current camera view; the key frame image sequence generation unit splits rendering scene data into key frame data sequences and generates key frame image sequences by combining an offline rendering device; the animation video synthesis unit fills the frame sequence through the frame interpolation to generate the final animation video.
The invention has the beneficial effects that: according to the method, an operator only needs to provide a rendering scene and a fixed view angle, a pre-calculation-planning module of the method can automatically calculate the filling sequence of elements in the scene to construct a frame sequence from an empty scene to a complete scene, the whole construction process is less than 20 seconds, no manual intervention is needed, and the traditional method needs the operator to conceive the content of each frame by frame, manually add and delete the elements in the scene, and the consumed time is different from dozens of minutes to hours; the rendering-generating-synthesizing module can automatically generate each frame of image and synthesized video according to the frame sequence, the whole process only needs several minutes, and the traditional method needs an operator to render all images frame by frame and often needs several hours; a plurality of links needing manual intervention in the prior art can be finished in an automatic mode, and the cost in all aspects is obviously reduced; the method performs targeted optimization on the field of indoor decoration, so that the method has higher application value.
Drawings
FIG. 1 is a flow chart of a furniture growth animation cloud rendering method based on a fixed viewing angle.
Fig. 2 is a configuration diagram of a furniture growth animation cloud rendering system based on a fixed viewing angle.
FIG. 3 is a screenshot of automatically synthesized growing animation video keyframes in a rendered scene for a fixed perspective of the present invention.
Description of reference numerals: a scene data preprocessing unit 101, a spatial position calculating unit 102, a growth sequence planning unit 103, a key frame image sequence generating unit 104, and an animation video synthesizing unit 105.
Detailed Description
The invention will be described in detail below with reference to the following drawings:
as shown in the attached drawings, the furniture growth animation cloud rendering method based on the fixed visual angle mainly comprises the following steps:
1) scene data preprocessing: acquiring the depth, mesh and other meta-information of a rendering object under the current camera view angle, and coding and storing the meta-information into two-dimensional data and necessary auxiliary indexes according to a pre-designed data format;
2) calculating the spatial position relation: deducing and establishing a spatial position relationship of the rendering object by utilizing the result data preprocessed in the step 1, and deducing and reconstructing a three-dimensional spatial position relationship of the rendering object from the two-dimensional meta-information, so that the calculation performance of the position relationship is greatly improved, and a context is provided for automatically planning a filling sequence;
3) planning a growth sequence: automatically planning the filling sequence of each element in the current camera view angle on the basis of the spatial position relation by combining the category marking information and other extended information of the rendering object;
4) generating a key frame image sequence: splitting rendering scene data into a key frame data sequence based on the filling sequence and other user input, and generating a key frame image sequence by combining an offline rendering device;
5) synthesizing an animation video: and filling the frame sequence through the frame interpolation to synthesize the final animation video.
The scene data preprocessing uses a prerendering method, three-dimensional space information in the current camera view angle is extracted into two-dimensional meta-information of a plurality of characteristic spaces, the prerendering method is different from the traditional illumination rendering, a beam of light is emitted to a scene through a light tracking or rasterization rendering algorithm (the light tracking algorithm, from the position of a camera, through a sampling position on an image plane, an intersection point of the light and a scene geometric model is obtained, the mesh index and the depth information of an intersection point model are obtained, the rasterization algorithm maps the geometric model bins of the scene onto the image plane of the camera one by one through coordinate transformation, each pixel point accesses the mesh index and the depth information of the bin closest to the camera, a final rendering image is obtained, only the meta-information such as mesh and depth value in the scene is extracted, the information is coded into two-dimensional data according to a pre-designed data format, and meanwhile, the calculation is convenient, the mapping relation between the mesh index and the rendering object is stored as a hash and used as an auxiliary index for calculation, and the attribute data of the rendering object comprises a unique id, so that the corresponding relation can be efficiently found.
The calculation of the spatial position relationship mainly comprises the following steps:
1) mapping the mesh meta information to depth information by using an SIMD vectorization method and a hash index, and further establishing mapping of each rendering object and a depth value matrix; specifically, reading a mesh bitmap and a depth bitmap, and mapping the mesh bitmap and the depth bitmap to a mesh matrix and a depth matrix; the mesh matrix is a gray value corresponding to each pixel point, is divided by different gray values in the preprocessing step, and constructs a hash index from a mesh unique id to the gray value; the depth matrix is the depth value corresponding to each pixel point. For each unique id, the pseudo code is expressed as follows:
inputting: m is a mesh matrix, N is a depth matrix and a gray index set;
and (3) outputting: dindexA depth matrix corresponding to each mesh;
the algorithm is as follows:
for index in (set of gray indices) }
Where the f function produces a logical matrix mapped to the boolean domain B {0,1}, which is defined as:
Further, we define the depth distance of each mesh as:
the spatial depth of the current camera view is defined as: s ═ max ((d (y)) -min (d (x)) |.
2) Dividing the space of the current camera view angle into a plurality of connected subspaces according to proper granularity (obtained by weighting and calculating the number of rendering objects and other features), dividing the rendering objects according to the depth distance of the subspaces, clustering the rendering objects, sequencing the rendering objects along the direction of a set space axis by using the depth distance in each subspace, and finally merging the sequencing results in the plurality of subspaces into the rendering object space position relation of the current scene. The specific algorithm flow is as follows:
1) dividing the spatial depth S into k subspaces according to the number N of rendering objects and a preset threshold value T,
2) traversing the depth distances of all rendering objects, judging the distance from the center point of the corresponding mesh depth distance to the center point of the appointed subspace, and putting the model into the subspace with the minimum distance;
3) recalculating and updating the subspace center points of all the category points processed in the step 2;
4) repeating the operations of the steps 2 and 3 until the central point of the subspace is stable or the specified iteration times are reached, and stopping;
5) in each subspace, using d (x), d (y) and | d (y) -d (x) | of mesh corresponding to each rendering object to perform multi-keyword sequencing, and combining sequencing results of all subspaces to obtain a global sequence.
And planning the filling sequence of each element in the current camera view angle by combining the category information of the rendering object and the user input based on the calculation result of the spatial position relationship. Including predefined category priority fill order rules, user-given desired fill orders, and potential combinatorial relationships between elements. The indoor decoration scene is optimized in a targeted mode, for example, the priority of category relations of hard clothes, soft clothes, furniture and the like is preset, so that the planning result is more consistent with the decoration arrangement implementation sequence in reality. Specifically, the invention automatically adds category labels to all elements in the rendered scene in combination with the service database. An implementation of a business rule sequence is presented herein:
1) blank room (eliminating all elements, only keeping house type modeling);
2) wall top coating and floor paving;
3) door pocket, door, window frame installation (including model, custom door, bim door);
4) customizing home assembly, a cabinet body, a table top, a top leg line and the like;
5) sofa, curtain cloth, soft ornament, etc.;
6) finally, based on the planning result, a key frame sequence is generated.
Generating a key frame image sequence and a composite animation video based on the key frame sequence, splitting scene data into the key frame data sequence, further generating the key frame image sequence by using an offline rendering device, taking the key frame image sequence as input, generating an interpolation frame by using an image processing method, finally exporting the interpolation frame in a streaming media file format, and delivering a user finished product by relying on public cloud distributed object storage service.
The furniture growth animation cloud rendering system based on the fixed visual angle mainly comprises a scene data preprocessing unit 101, a spatial position calculating unit 102, a growth sequence planning and planning unit 103, a key frame image sequence generating unit 104 and an animation video synthesizing unit 105, wherein the scene data preprocessing unit 101 obtains the depth, mesh and other meta-information of a rendering object under the current camera visual angle; the spatial position calculation unit 102 deduces the result data of the scene data preprocessing unit 101 to establish a spatial position relationship of the rendering object; the growth sequence planning unit 103 automatically plans the filling sequence of each element in the current camera view; the key frame image sequence generating unit 104 splits the rendering scene data into a key frame data sequence, and generates a key frame image sequence by combining an offline rendering device; the animation video synthesis unit 105 fills the frame sequence by interpolation to generate a final animation video. Each unit can be split and deployed in a distributed computing environment, resource bottlenecks are eliminated by utilizing a multi-computing node parallel architecture, and computing efficiency is improved; closely related units may also be collocated to improve the performance of data exchange.
It should be understood that equivalent substitutions and changes to the technical solution and the inventive concept of the present invention should be made by those skilled in the art to the protection scope of the appended claims.
Claims (6)
1. A furniture growth animation cloud rendering method based on a fixed visual angle is characterized by comprising the following steps: the method mainly comprises the following steps:
1) scene data preprocessing: acquiring the depth, mesh and other meta-information of a rendering object under the current camera view angle, and coding and storing the meta-information into two-dimensional data and necessary auxiliary indexes according to a pre-designed data format;
2) calculating the spatial position relation: deducing and establishing a spatial position relationship of the rendering object by utilizing the result data preprocessed in the step 1, deducing and reconstructing a three-dimensional spatial position relationship of the rendering object from the two-dimensional meta-information, and providing context for automatically planning a filling sequence;
3) planning a growth sequence: automatically planning the filling sequence of each element in the current camera view angle on the basis of the spatial position relation by combining the category marking information and other extended information of the rendering object;
4) generating a key frame image sequence: splitting rendering scene data into a key frame data sequence based on the filling sequence and other user input, and generating a key frame image sequence by combining an offline rendering device;
5) synthesizing an animation video: and filling the frame sequence through the frame interpolation to synthesize the final animation video.
2. The fixed-view-angle-based furniture-growth animation cloud rendering method according to claim 1, wherein: the scene data preprocessing adopts a pre-rendering method, three-dimensional space information in a current camera view angle is extracted into two-dimensional meta-information of a plurality of characteristic spaces, the pre-rendering method stores a mapping relation between a mesh index and a rendering object as a hash through a ray tracing or rasterization rendering algorithm, the hash is used as a calculated auxiliary index, and attribute data of the rendering object contains a unique id.
3. The fixed-view-angle-based furniture-growth animation cloud rendering method according to claim 1, wherein: the calculation of the spatial position relationship mainly comprises the following steps:
1) mapping the mesh meta information to depth information by using an SIMD vectorization method and a hash index, and further establishing mapping of each rendering object and a depth value matrix;
2) the space of the current camera view angle is divided into a plurality of connected subspaces with proper granularity, the rendered objects are clustered according to the depth distance division of the subspaces, the rendered objects are sequenced along the direction of a set spatial axis by using the depth distance in each subspace, and finally the sequencing results in the plurality of subspaces are merged into the spatial position relation of the rendered objects of the current scene.
4. The fixed-view-angle-based furniture-growth animation cloud rendering method according to claim 1, wherein: the growing order plan includes predefined category priority filling order rules, a desired filling order given by the user, and potential composition relationships between elements.
5. The fixed-view-angle-based furniture-growth animation cloud rendering method according to claim 1, wherein: the method comprises the steps of generating a key frame image sequence and a composite animation video, splitting scene data into a key frame data sequence based on the key frame sequence, further generating the key frame image sequence by using an offline rendering device, taking the key frame image sequence as input, generating an interpolation frame by using an image processing method, finally exporting the interpolation frame in a streaming media file format, and delivering a user finished product by relying on public cloud distributed object storage service.
6. The utility model provides a furniture growth animation cloud system of rendering based on fixed visual angle which characterized in that: the method mainly comprises a scene data preprocessing unit (101), a spatial position calculating unit (102), a growth sequence planning and planning unit (103), a key frame image sequence generating unit (104) and an animation video synthesizing unit (105), wherein the scene data preprocessing unit (101) obtains the depth, mesh and other meta-information of a rendering object under the current camera view angle; the spatial position calculation unit (102) deduces the result data of the scene data preprocessing unit (101) to establish the spatial position relationship of the rendering object; a growth sequence planning unit (103) automatically plans the filling sequence of each element in the current camera view; a key frame image sequence generation unit (104) splits rendering scene data into key frame data sequences, and generates key frame image sequences by combining an offline rendering device; an animation video synthesis unit (105) generates a final animation video by filling the frame sequence with frame interpolation.
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