CN112734930A - Three-dimensional model weight reduction method, system, storage medium, and image processing apparatus - Google Patents
Three-dimensional model weight reduction method, system, storage medium, and image processing apparatus Download PDFInfo
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Abstract
A three-dimensional model weight reduction method, a system, a storage medium, and an image processing apparatus, the weight reduction method comprising: loading an original model file, and reading original model data, wherein the original model data comprises: model mesh, point number, face number and model size; carrying out pre-grid optimization processing on the original model to generate a pre-optimization grid model with uniform wiring; carrying out surface reduction treatment on the preposed optimization grid model to obtain a surface reduction grid model after the surface reduction treatment; carrying out UV (ultraviolet) unfolding treatment on the surface-reduced grid model, and splitting elements of a model element and a mapping to obtain a non-mapping 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 mapping set. The technical scheme provided by the application can greatly shorten the large model lightweight processing time, improve the model lightweight ratio, 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 lightweight method, in particular to a model lightweight method based on a mesh topology algorithm, belonging to the technical field of image processing; the invention also relates to a model lightweight system based on the mesh 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 larger the size and the complexity of a model are, the greater the difficulty of model processing is, most of the model processing adopts manual processing, the processing speed of the mode is low, and the processing capacity of the model is limited. The large model and the complex model are quickly processed, the model can be reduced as much as possible, the model after being lightened can be quickly 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 lightweight method which can advantageously increase the speed of the next stage development UV treatment and baking treatment; the method greatly shortens the processing time of the large model light weight, improves the model light weight ratio, and realizes that the model is easier to open and edit in three-dimensional software and a client, and is a technical problem to be solved urgently by technical personnel in the field.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to solve the problems of opening and editing of a large model in various three-dimensional software and clients. The invention provides a model lightweight method based on a mesh 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, point number, face number and model size; carrying out pre-grid optimization processing on the original model to generate a pre-optimization grid model with uniform wiring; carrying out surface reduction treatment on the preposed optimization grid model to obtain a surface reduction grid model after the surface reduction treatment; carrying out UV (ultraviolet) unfolding treatment on the surface-reduced grid model, and splitting elements of a model element and a mapping to obtain a non-mapping 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 mapping set.
According to a first embodiment of the present invention, there is provided a model lightweight method based on mesh topology algorithm:
a model lightweight method based on a mesh topology algorithm comprises the following steps:
loading an original model file, and reading original model data, wherein the original model data comprises: model mesh, point number, face number and model size;
carrying out pre-grid optimization processing on the original model to generate a pre-optimization grid model with uniform wiring;
carrying out surface reduction treatment on the preposed optimization grid model to obtain a surface reduction grid model after the surface reduction treatment;
carrying out UV (ultraviolet) unfolding treatment on the surface-reduced grid model, and splitting elements of a model element and a mapping to obtain a non-mapping 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 mapping set.
Further, as a more preferred embodiment of the present invention, the pre-mesh optimization process includes the steps of:
performing basic optimization processing on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped models to obtain a basic optimization model;
and carrying out grid normalization processing on the basic optimization model, and constructing a uniform smooth grid on the basic optimization model to obtain a preposed optimization grid model.
It should be noted that:
1) model file: 3D model (original model) is a file describing a three-dimensional space
2) The mesh topology is the point-line-surface layout, structure and connection of the polygonal mesh model. If a model has a good topological structure, not only the model wiring appearance can be more clean and regular, but also to a great extent, the processing efficiency of the model is improved, the light-weight speed is accelerated and the light-weight capability is improved.
3) The unfolding UV treatment is a process of reasonably spreading the UV texture surface of the original model on a two-dimensional canvas (UV texture set) so as to be reasonably distributed.
4) The baking (Bake) process is a process of saving the geometric features of the 3D mesh to a texture file (bitmap file). Multiple combined properties (including material, texture, and lighting) are baked from the 3D object attributes (ambient light shielding, normal, vertex color, orientation, curvature, position, etc.) into a single texture (texture atlas), which in turn can be used to remap the image texture to the model object.
Further, as a more preferred embodiment of the present invention, the "constructing a uniform smooth mesh on the basic optimization model" specifically includes: 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 includes the steps of:
reading the basic optimization model, constructing a defined neighborhood relationship of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
constructing an optimized direction field and a position field;
carrying out grid extraction processing, converting the calculation field into a grid, and obtaining a grid structure;
outputting the pre-optimization grid model, wherein the pre-optimization grid model comprises the following steps: the method comprises the steps of reserving a preposed optimization grid model of a structure and not reserving the preposed optimization grid model of the structure.
Further, as a more preferred embodiment of the present invention, the method for outputting the pre-optimization mesh model of the retention structure comprises: and carrying out topology optimization on all grid objects in the file according to the pre-recorded model structure of the original model, which is recorded in advance, and carrying out file restoration according to the model structure to generate a pre-optimization grid model with a reserved structure.
Further, as a more preferred embodiment of the present invention, the method for outputting the pre-optimized mesh model without preserving the structure comprises: and merging all grid objects of the original model, performing topology optimization, and directly generating a preposed optimization grid model without a reserved structure.
Further, as a more preferable embodiment of the present invention, the method for reducing weight further includes:
reading and converting the non-map model into a target model file according to a target file format;
reading and converting the texture mapping set into a target mapping 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 mesh topology algorithm:
a mesh topology algorithm based model lightweight system, the system comprising:
the loading and reading device is used for loading the original model file and reading original model data;
the pre-grid optimization processing device is used for performing pre-grid optimization processing on the original model and generating a pre-optimization grid model with uniform wiring;
the surface reduction processing device is used for carrying out surface reduction processing on the preposed optimization grid model and obtaining a surface reduction grid model after the surface reduction processing;
the unfolding UV treatment device is used for carrying out unfolding UV treatment on the minus mesh model, splitting elements of model elements and a mapping to obtain a non-mapping model and a UV texture set;
and the baking treatment device is used for baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture mapping set.
According to a third embodiment of the present invention, there is provided a computer-readable storage medium:
a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out 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 executing the computer program.
Compared with the prior art, the technical scheme of the application has the following technical effects:
1. according to the technical scheme, the UV utilization rate is improved, the best effect of the model can be represented on the premise that the mapping precision specification is limited, mapping resources can be saved, and the model can run more smoothly on the terminal equipment.
Drawings
FIG. 1 is a flowchart of a model lightweight method based on a mesh topology algorithm in an embodiment of the present 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 an optimized direction field constructed in an embodiment of the present invention;
FIG. 4 is a schematic diagram of an optimized location field constructed in an embodiment of the present invention;
FIG. 5 is a diagram illustrating grid normalization in accordance with an embodiment of the present invention;
FIG. 6 is a schematic diagram of the included angle of the triangular faces connected in the embodiment of the present invention
FIG. 7 is a schematic diagram of an ungrid original model mesh in an embodiment of the present invention,
segmentation lines of the ungrid original model mesh: s1, the first face to be developed block of the ungrid original model mesh: c1, second face to be spread block of ungrid original model mesh: c2;
FIG. 8 is a schematic diagram of a gridded topology model grid according to an embodiment of the present invention,
and (3) dividing lines of the meshed topological model mesh: s2, the first to-be-developed surface block of the meshed topological model mesh: d1, the second face block to be displayed of the meshed topological model mesh: d2;
FIG. 9 is a UV plan view of an ungrid original model after UV exposure in an embodiment of the present invention,
un-gridded original model UV block after UV unfolding: WGZ1, ungrid original model exhibits voids after UV: KX1, a first un-gridded UV block C1 'after UV is spread on a first un-gridded face block C1 of an un-gridded original model grid, and a second un-gridded UV block C2' after UV is spread on a second un-gridded face block C2 of the un-gridded original model grid;
FIG. 10 is an enlarged collective view of all the UV blocks of FIG. 9, with the spaces between the UV blocks reduced;
FIG. 11 is a UV plan view of the gridded topology model after UV exposure in the embodiment of the present invention,
and (3) displaying the UV block after UV by the gridded topological model: WGZ2, the gridded topology model develops a null after UV: KX2, a first surface to be developed block D1 of the gridded topology model grid, a first UV block D1' after UV development,
a gridded second UV block D2' after UV is spread by a second surface block D2 to be spread of the gridded topology model grid;
FIG. 12 is a diagram of prior art grid normalization;
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 diagram according to an embodiment of the present invention;
FIG. 15 is a simplified diagram of a Mesh enlarged area grid in an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solutions in the present application, the technical solutions in 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 obvious that the described embodiments are only a part of the embodiments of the present application, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present 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 disposed 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 will be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like, refer to an orientation or positional relationship illustrated in the drawings for convenience in describing the present application and to simplify description, and do not indicate or imply that the referenced device or element must have a particular orientation, be constructed and operated in a particular orientation, and thus should not be construed as limiting the present application.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of that feature. In the description of the present application, "plurality" or "a plurality" means two or more unless specifically limited otherwise.
It should be understood that the structures, ratios, sizes, and the like shown in the drawings are only used for matching the disclosure of the specification, so as to be understood and read by those skilled in the art, and are not used to limit the practical limit conditions of the present application, so that the modifications of the structures, the changes of the ratio relationships, or the adjustment of the sizes, do not have the technical essence, and the modifications, the changes of the ratio relationships, or the adjustment of the sizes, are all within the scope of the technical contents disclosed in the present application without affecting the efficacy and the achievable purpose of the present application.
According to a first embodiment of the present invention, there is provided a model lightweight method based on mesh topology algorithm:
a model lightweight method based on a mesh topology algorithm comprises the following steps: loading an original model file, and reading original model data, wherein the original model data comprises: model mesh, point number, face number and model size;
carrying out pre-grid optimization processing on the original model to generate a pre-optimization grid model with uniform wiring;
carrying out surface reduction treatment on the preposed optimization grid model to obtain a surface reduction grid model after the surface reduction treatment;
carrying out UV (ultraviolet) unfolding treatment on the surface-reduced grid model, and splitting elements of a model element and a mapping to obtain a non-mapping 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 mapping set.
The application provides a model lightweight method based on a mesh topology algorithm. After loading an original model and reading original model data, performing pre-optimization processing on the original model to generate a pre-optimization grid model with uniform wiring, and then performing surface reduction processing on the pre-optimization grid model to obtain a surface reduction grid model after the surface reduction processing; the method comprises the steps that a pre-mesh optimization processing process is carried out on the surface of a model, wherein a pre-mesh optimization model with uniform wiring is generated in the pre-mesh optimization processing process, and due to the uniform mesh model, a chartlet can be rapidly projected along the discovery vertical to the surface in the UV unfolding process of the surface material of the model, so that the speed of the UV unfolding processing and the baking processing in the next stage can be greatly improved; the processing time of large model lightweight is greatly shortened, the model lightweight ratio is improved, and the model is easier to open and edit in three-dimensional software and a client.
It should be noted that "UV" herein refers to a abbreviation for u, v texture map coordinates (which are similar to the X, Y, Z axes of the spatial model). Which defines information of the position of each point on the picture. The points are interconnected with the 3D model to determine the position of the surface texture map, UV is the exact mapping of each point on the image to the surface of the model object, the position of the gaps between points is interpolated smoothly by the software, which is the so-called UV map.
It should be noted that the model general processing method: the model process flow includes facelift, UV development and bake. Because the model pre-optimization can generate a topology model with uniform rules, nodes in the processing flow can be flexibly selected, such as single-node processing (face reduction), and such as combined processing (spreading UV and baking) of a plurality of nodes.
It should be noted that, the unfolding UV treatment actually improves the accuracy of the finite-size chartlet, so that the model and the chartlet are perfectly matched; the method is an idea provided for reducing resource waste, changes the previous one-to-one relation into one-to-many relation, reduces repeated UV parts, greatly improves the UV utilization rate, and reduces resources occupied by the map.
Specifically, in the embodiment of the invention, the model performs the face reduction processing according to the read face reduction parameters.
Specifically, in the present example, UV (UV development treatment): and spreading the UV texture on the model, reading the model and tiling all the UV maps in the Mesh in the model on the UV texture.
Specifically explaining, in the present embodiment, baking (baking treatment): mapping colors of the model high mode and the model low mode for mapping to UV, baking the model texture after the UV is spread to generate a corresponding texture mapping, and endowing the mapping to the model. Two ways of baking are provided: firstly, rendering basic colors, normals and illumination on model textures in a traditional mode, and generating color maps and normal maps; the second, sub-generation model allows for PBR baking and high light baking. Color mapping, normal mapping, AO mapping, metallization mapping and roughness mapping can be generated by PBR baking; the highlight baking can generate color maps, normal maps, AO maps, and highlight maps.
Specifically, in an embodiment of the present invention, the pre-mesh optimization processing includes the following steps: performing basic optimization processing on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped models to obtain a basic optimization model; and carrying out grid normalization processing on the basic optimization model, and constructing a uniform smooth grid on the basic optimization model to obtain a preposed optimization grid model.
It should be noted that, in the basic optimization process, by deleting repeated vertices of the model, repairing joints of the model, and removing the model at the overlapped part, the original model is internally fused into a whole (original contour whole) while maintaining the shape structure unchanged, and the original contour whole reduces a large amount of data of repeated vertices, lines, and overlapped entities compared with the original model on the basis data. For example: the original model is a cup body and a cup cover of the vacuum cup; the state that the thermos cup appears is that the cup body covers on the cup cover, and then the screw thread structure of the cup body and the cup cover connecting part is the coincidence part, and is redundant summits or lines. Through basic optimization processing, the original model of the vacuum cup is optimized to be the data of the whole original outline of the vacuum cup appearance (shell) data, so that the data structure of the original model is greatly optimized, the data volume is reduced, the difficulty of gridding the original model in the later period is reduced, and the speed of gridding, UV (ultraviolet) unfolding processing and baking processing in the later period is increased.
It should be noted that the mesh normalization process is specifically to construct a uniform and smooth mesh through a mesh topology algorithm; the 'construction of uniform and smooth grids' is a triangular surface grid. In the prior art, generally, in order to improve the smoothness of the modeled entity after gridding, as shown in fig. 5, a surface a1 and a surface B2 form a rectangular surface, but the rectangular surface is spatially folded in half according to a dotted line Y, and the rectangular surface is not smooth. When the new dotted line Y is added to split the four corner surfaces into 2 triangular surfaces, a surface a1 and a surface B2 are generated, and the 2 surfaces are smooth. Gridding was performed using different numbers of corner faces for the contours of different entities, as shown in fig. 12-12: triangular surface J3, quadrangular surface J4, pentagonal surface J5, and the like. In the application scenario of the scheme, the model file processed by the model lightweight method based on the mesh topology algorithm is mainly used for displaying on a small-screen terminal. In the process of displaying the model file processed by the scheme, the small-screen terminal has a certain distance from human eyes, and the content of the model file displayed at the same time has continuous displacement action in the display area, so that the use scene of the scheme determines that the precision requirement on the model file which needs to be loaded and is processed and optimized is not high. Therefore, in the scheme of the application, the basic optimization model is optimized into a pre-optimization grid model which is a triangular surface grid, so that the UV unfolding treatment can be conveniently carried out later.
Furthermore, after the grids are unified into the triangular surface, the system does not need to judge the processing of the angular surface of the grids during subsequent operation, namely judging whether the grid is the triangular surface or the rectangular surface currently; meanwhile, the system does not need to call complex functions to process the four-corner surface or the pentagon surface; the method greatly accelerates the overall processing speed, optimizes the data flow of the original model file in the process of executing the method provided by the application, and promotes the development of subsequent processing.
It should be noted that the pre-mesh optimization process: the method mainly comprises two steps, wherein the first step is model basic optimization, and the second step is grid normalization. The normalized optimization model is beneficial to the subsequent process, and a lot of problems are encountered in the process of developing the UV, for example, Mesh wiring is very disordered and is very slow in the process of automatically developing the UV, and it is assumed that if one model has 30 ten thousand surfaces, the model needs to be calculated 30 ten thousand times in the process of processing the development of the calculation process. And the normalized optimization optimizes the surface number to 10 ten thousand surfaces. Only 10 ten thousand calculations are needed during the flattening process. The number of calculations is reduced after normalization. Also, for example, if the wiring is not uniform during the calculation, many fragmented surfaces may be cut out when the cut surface is flattened, and the space occupancy of UV becomes low. The calculation process of UV is also slowed down and calculation time is wasted due to the fact that many fragmented surfaces are flattened. If the spread occupancy of UV is higher through the uniform wiring after normalization, the generation of the subsequent baking mapping chart, AO mapping and highlight mapping chart is faster, and the mapping quality is finer.
It should be noted that the basic optimization processing specifically includes: deleting repeated vertexes in the original model mesh, and repairing the joint of the mesh and removing the duplication; the basic optimization process further includes: and repairing the abnormal model.
Preferably, in an embodiment of the present invention, the UV treatment comprises the following steps:
counting the grid data information of the reduced-surface grid model;
and (4) tiling the surface grid of the reduced-surface grid model on a two-dimensional canvas in a tiling and unfolding mode to obtain a UV texture set.
As shown in fig. 6, the mesh formed by the mesh normalization processing is a triangular mesh; from a microscopic perspective, only 3 sides of 1 triangular mesh are connected with the peripheral 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 corner surfaces, five corner surfaces and the like larger than three corner surfaces, the grid structure is more beneficial to the processing of the terminal processor. Namely, the step of performing pre-mesh optimization processing on the original model in the application scheme of the application is greatly beneficial to the step of performing surface reduction processing on the pre-mesh optimization model; has great promoting effect.
Specifically, in the embodiment of the present invention, the "constructing a uniform smooth mesh on the basic optimization model" specifically includes: 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 during UV stretching. If the original model is directly subjected to UV expansion after the original model is loaded, the original model contains too many repeated point, line and plane data, and compared with the scheme of the application, the following problems are caused: firstly, as can be seen from comparison between fig. 7 and 8, the area of a single grid is small, the number of grids is large, and a large number of operations are required in the UV unfolding process; secondly, as can be seen from comparison between fig. 9 and fig. 11, due to the complex structure, a large number of UV blocks are separated during UV spreading, each UV block includes a plurality of single grids, which reduces the baking efficiency during the baking operation at the later stage; thirdly, as can be seen from the comparison between fig. 9 and fig. 11, in the baking process, the UV texture sets need to be addressed, and the void ratio between each UV block is large, which results in too large addressing time and further causes low efficiency of the baking process; fourthly, as can be seen from comparison between fig. 9 and fig. 11, the void ratio between each UV block is large, so that the area of the UV texture set in the prior art is large, 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 the comparison between fig. 9 and fig. 11, under the premise of the same resolution of the UV texture set, the resolution of the single grid in the prior art is lower than that of the solution of the present application due to the large void ratio between each UV block.
Based on the reason, according to the scheme, the initial model data is subjected to the pre-grid optimization treatment to generate the pre-optimization grid model with uniform wiring, so that the post-development UV treatment and baking carding of the grid data can be greatly facilitated, the overall treatment speed is increased, and the treatment quality is improved.
It should be further noted that, from the two Mesh objects shown in fig. 7 and 8, the routing S2 and the number of planes in fig. 8 are significantly better than those in fig. 7, and the routing S1 in fig. 7 is messy and complicated. Fig. 8 shows the Mesh object obtained by the normalized optimization, and the dark black line is the calculated segmentation line S2 of the spread UV, and the program flattens the model with reference to the segmentation line. Fig. 8 restores some errors (continuity of surfaces, isolated points, broken surfaces, etc.) possibly existing in the original Mesh due to the optimized number of surfaces of the model, and reconstructs the Mesh to obtain a new Mesh object. Generally, the usage rate of the post-baking map is determined by the general UV map, fig. 9 is the UV map obtained after UV is developed by the original model, fig. 11 is the UV map obtained after normalization, it can be seen that the usage rate of pixels in the picture of 1920 × 1920 in fig. 11 is obviously higher than that in fig. 9, that is, the picture produced by fig. 11 after baking has higher definition than that in fig. 9. The UV utilization of fig. 11 is also significantly higher than fig. 9. Whether the model's wiring is averaged relates to whether the UV unfolding is flat, and the space of the UV makes reasonable use of the mapping of the concerns map.
Specifically, in the embodiment of the present invention, the mesh topology algorithm includes the following steps:
reading the basic optimization model, constructing a defined neighborhood relationship of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
constructing an optimized direction field and a position field;
carrying out grid extraction processing, converting the calculation field into a grid, and obtaining a grid structure;
outputting the pre-optimization grid model, wherein the pre-optimization grid model comprises the following steps: the method comprises the steps of reserving a preposed optimization grid model of a structure and not reserving the preposed optimization grid model of the structure.
Specifically, in the embodiment of the present invention, the method for outputting the pre-optimization mesh model of the retention structure includes: and carrying out topology optimization on all grid objects in the file according to the pre-recorded model structure of the original model, which is recorded in advance, and carrying out file restoration according to the model structure to generate a pre-optimization grid model with a reserved structure.
Specifically, in the embodiment of the present invention, the method for outputting the pre-optimization mesh model without preserving the structure includes: and merging all grid objects of the original model, performing topology optimization, and directly generating a preposed optimization grid model without a reserved structure.
Specifically, in an embodiment of the present invention, the method for reducing weight further includes:
reading and converting the non-map model into a target model file according to a target file format;
reading and converting the texture mapping set into a target mapping file according to a target file format;
and storing the target model file and the target map file into a memory.
Note that, the output: the system supports various output modes, and supports different format output and 360-degree picture output of the model. First, the format (object model file) outputs: reading an externally input target format, reading the topological model, and performing format conversion on the baked topological model according to the target format to generate a target model (containing a target map file); second, 360 ° picture (model picture generated from different angle rendering): reading the topological model, reading the number of externally input pictures, rendering the baked topological model according to 360 degrees according to the number of the pictures, and generating the pictures with corresponding numbers.
According to a second embodiment of the present invention, there is provided a model lightweight system based on mesh topology algorithm:
a mesh topology algorithm based model lightweight system, the system comprising:
the loading and reading device is used for loading the original model file and reading original model data;
the pre-grid optimization processing device is used for performing pre-grid optimization processing on the original model and generating a pre-optimization grid model with uniform wiring;
the surface reduction processing device is used for carrying out surface reduction processing on the preposed optimization grid model and obtaining a surface reduction grid model after the surface reduction processing;
the unfolding UV treatment device is used for carrying out unfolding UV treatment on the minus mesh model, splitting elements of model elements and a mapping to obtain a non-mapping model and a UV texture set;
and the baking treatment device is used for baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture mapping set.
According to a third embodiment of the present invention, there is provided a computer-readable storage medium:
a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out 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 executing the computer program.
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 network transmission speed and rendering speed. The technical scheme adopted by the invention is as follows: firstly, model pre-optimization is carried out on an original model on the basis of shape preservation, a gridding model is reconstructed, and then a general model processing flow is executed on the gridding model: and reducing the surface, spreading UV and baking to finally realize the lightweight treatment of the model.
It should be noted that model basis optimization (basis optimization processing): deleting repeated vertexes in the Mesh, repairing joints of the Mesh and removing the duplicate, repairing abnormal models (such as abnormal material and isolated point Mesh),
mesh normalization (mesh normalization processing): mesh repartitioning an isotropic curved surface uses unified local smooth triangle or quadrilateral main Mesh to optimize operators of edge direction and vertex position in output Mesh, and finally converts the original digitized polygon Mesh into a clean normalized Mesh. The scheme mainly applies a grid topology algorithm, the topology algorithm combines the ideas of local and global gridding methods, the local direction and position field smoothing algorithm is used for calculating a grid which is globally aligned with a direction field, then the grid is extracted from a field, and post-processing is carried out.
The mesh topology algorithm mainly comprises the following steps: reading a model: we store the input surface model with a set of edges where each vertex is associated with a position and a normal direction, and define the neighborhood relationships in various ways from 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 input.
Orientation field optimization (constructing an optimized orientation field): the orientation field is computed because the word can know the alignment of the edges in the final grid. The orientation field satisfies the rotational symmetry condition of degrees, which means that each vertex is associated with a set of unit lengths, and the values associated with uniformly spaced tangent vectors can be reduced to the conventional tangent vector field.
Wherein the first expression in FIG. 3 is to rotate o around the normal vector n
The second expression, the angle, is to form an n-Rosy direction field with o symmetric rotations n times at the vertex.
Location field optimization (building an optimized location field): given the oriented RoSy field O (computed using our method or other field design algorithm) we now compute a local parameterization whose gradient is aligned with O, as in fig. 4. The global parameterization algorithm computes a single uniform parameterization whose gradient matches the direction field in the least squares sense;
two plots of positional field smoothness energy: inherently, all vj related quantities are rotated into the tangent plane of vi; thereafter, the representative location closest to pi is determined. In the outer case, the rotation will be omitted and both positions will be translated. The final representative positions are drawn in dark.
And (3) outputting: and dividing the model into a reserved structure and a non-reserved structure, and judging and generating a reserved structure model or a non-reserved structure model according to external input parameters.
It should be noted that, as the cutting line is generated by performing UV calculation according to the original model of fig. 7, it is obvious from the cutting line represented by the darkened black line that the Mesh object of fig. 7 is divided into a plurality of UV blocks in fig. 9 by the UV development software.
The same UV software as that in fig. 7 is used to perform UV calculation on fig. 8 to generate cut lines, and it is obvious from the cut lines indicated by the darkened black lines that the Mesh objects in fig. 8 are divided into only 4 Mesh objects.
The range of the bold line in fig. 14 is just the Mesh enlarged area, after the Mesh topology, the Mesh in the range of the bold line 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 fig. 11 is formed by the same UV algorithm.
Example 1
A model lightweight method based on a mesh topology algorithm comprises the following steps:
loading an original model file, and reading original model data, wherein the original model data comprises: model mesh, point number, face number and model size;
carrying out pre-grid optimization processing on the original model to generate a pre-optimization grid model with uniform wiring;
carrying out surface reduction treatment on the preposed optimization grid model to obtain a surface reduction grid model after the surface reduction treatment;
carrying out UV (ultraviolet) unfolding treatment on the surface-reduced grid model, and splitting elements of a model element and a mapping to obtain a non-mapping 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 mapping set.
Example 2
Example 1 is repeated except that the pre-mesh optimization process comprises the following steps:
performing basic optimization processing on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped models to obtain a basic optimization model;
and carrying out grid normalization processing on the basic optimization model, and constructing a uniform smooth grid on the basic optimization model to obtain a preposed optimization grid model.
Example 3
Example 2 is repeated, except that the "constructing a uniform smooth mesh on the basic optimization model" specifically includes: and constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm.
Example 4
Embodiment 3 is repeated except that the mesh topology algorithm comprises the following steps:
reading the basic optimization model, constructing a defined neighborhood relationship of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
constructing an optimized direction field and a position field;
carrying out grid extraction processing, converting the calculation field into a grid, and obtaining a grid structure;
outputting the pre-optimization grid model, wherein the pre-optimization grid model comprises the following steps: the method comprises the steps of reserving a preposed optimization grid model of a structure and not reserving the preposed optimization grid model of the structure.
Example 5
The embodiment 4 is repeated, except that the method for outputting the pre-optimization mesh model of the retention structure comprises the following steps: and carrying out topology optimization on all grid objects in the file according to the pre-recorded model structure of the original model, which is recorded in advance, and carrying out file restoration according to the model structure to generate a pre-optimization grid model with a reserved structure.
Example 6
Example 5 is repeated, except that the method for outputting the pre-optimized mesh model without preserving the structure comprises the following steps: and merging all grid objects of the original model, performing topology optimization, and directly generating a preposed optimization grid model without a reserved structure.
Example 7
Example 1 was repeated except that the weight reduction method further included:
reading and converting the non-map model into a target model file according to a target file format;
reading and converting the texture mapping set into a target mapping file according to a target file format;
and storing the target model file and the target map file into a memory.
Example 8
A mesh topology algorithm based model lightweight system, the system comprising:
the loading and reading device is used for loading the original model file and reading original model data;
the pre-grid optimization processing device is used for performing pre-grid optimization processing on the original model and generating a pre-optimization grid model with uniform wiring;
the surface reduction processing device is used for carrying out surface reduction processing on the preposed optimization grid model and obtaining a surface reduction grid model after the surface reduction processing;
the unfolding UV treatment device is used for carrying out unfolding UV treatment on the minus mesh model, splitting elements of model elements and a mapping to obtain a non-mapping model and a UV texture set;
and the baking treatment device is used for baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture mapping set.
Example 10
A computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to 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 executing the computer program.
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 (10)
1. A model lightweight method based on a mesh topology algorithm is characterized by comprising the following steps:
loading an original model file, and reading original model data, wherein the original model data comprises: model mesh, point number, face number and model size;
carrying out pre-grid optimization processing on the original model to generate a pre-optimization grid model with uniform wiring;
carrying out surface reduction treatment on the preposed optimization grid model to obtain a surface reduction grid model after the surface reduction treatment;
carrying out UV (ultraviolet) unfolding treatment on the surface-reduced grid model, and splitting elements of a model element and a mapping to obtain a non-mapping 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 mapping set.
2. The mesh topology algorithm-based model lightweight method according to claim 1, wherein the pre-mesh optimization process comprises the steps of:
performing basic optimization processing on the original model, deleting repeated vertexes of the model, repairing joints of the model, and removing overlapped models to obtain a basic optimization model;
and carrying out grid normalization processing on the basic optimization model, and constructing a uniform smooth grid on the basic optimization model to obtain a preposed optimization grid model.
3. The method for model weight reduction based on mesh topology algorithm according to claim 2, wherein the "constructing a uniform smooth mesh on the basic optimization model" specifically comprises: and constructing a uniform smooth grid on the basic optimization model through a grid topology algorithm.
4. The mesh topology algorithm-based model lightweight method according to claim 3, wherein the mesh topology algorithm comprises the steps of:
reading the basic optimization model, constructing a defined neighborhood relationship of vertexes, positions and normal directions, and setting uniformity weights of adjacent vertexes;
constructing an optimized direction field and a position field;
carrying out grid extraction processing, converting the calculation field into a grid, and obtaining a grid structure;
outputting the pre-optimization grid model, wherein the pre-optimization grid model comprises the following steps: the method comprises the steps of reserving a preposed optimization grid model of a structure and not reserving the preposed optimization grid model of the structure.
5. The mesh topology algorithm-based model weight reduction method according to claim 4, wherein the method of outputting the pre-optimized mesh model of the retention structure comprises: and carrying out topology optimization on all grid objects in the file according to the pre-recorded model structure of the original model, which is recorded in advance, and carrying out file restoration according to the model structure to generate a pre-optimization grid model with a reserved structure.
6. The mesh topology algorithm-based model weight reduction method according to claim 4, wherein the method of outputting the pre-optimized mesh model without preserving the structure comprises: and merging all grid objects of the original model, performing topology optimization, and directly generating a preposed optimization grid model without a reserved structure.
7. The mesh topology algorithm-based model weight reduction method according to any one of claims 1 to 6, further comprising:
reading and converting the non-map model into a target model file according to a target file format;
reading and converting the texture mapping set into a target mapping file according to a target file format;
and storing the target model file and the target map file into a memory.
8. A model lightweight system based on mesh topology algorithm, characterized in that the system comprises:
the loading and reading device is used for loading the original model file and reading original model data;
the pre-grid optimization processing device is used for performing pre-grid optimization processing on the original model and generating a pre-optimization grid model with uniform wiring;
the surface reduction processing device is used for carrying out surface reduction processing on the preposed optimization grid model and obtaining a surface reduction grid model after the surface reduction processing;
the unfolding UV treatment device is used for carrying out unfolding UV treatment on the minus mesh model, splitting elements of model elements and a mapping to obtain a non-mapping model and a UV texture set;
and the baking treatment device is used for baking the UV texture set, and mapping the color of the reduced-surface grid model to the UV texture set to obtain a texture mapping set.
9. A computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when being executed by a processor, carries out the steps of the method as claimed in claims 1-7.
10. An image processing apparatus characterized in that the terminal device includes: memory, processor and computer program stored in the memory and executable on the processor, the processor implementing the steps of the method as claimed in claims 1-7 when executing the computer program.
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