CN110738721A - Three-dimensional scene rendering acceleration method and system based on video geometric analysis - Google Patents
Three-dimensional scene rendering acceleration method and system based on video geometric analysis Download PDFInfo
- Publication number
- CN110738721A CN110738721A CN201910969273.XA CN201910969273A CN110738721A CN 110738721 A CN110738721 A CN 110738721A CN 201910969273 A CN201910969273 A CN 201910969273A CN 110738721 A CN110738721 A CN 110738721A
- Authority
- CN
- China
- Prior art keywords
- model
- rendering
- dimensional scene
- nodes
- video
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T15/00—3D [Three Dimensional] image rendering
- G06T15/005—General purpose rendering architectures
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02D—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN INFORMATION AND COMMUNICATION TECHNOLOGIES [ICT], I.E. INFORMATION AND COMMUNICATION TECHNOLOGIES AIMING AT THE REDUCTION OF THEIR OWN ENERGY USE
- Y02D10/00—Energy efficient computing, e.g. low power processors, power management or thermal management
Landscapes
- Engineering & Computer Science (AREA)
- Computer Graphics (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Image Generation (AREA)
Abstract
The invention relates to the technical field of three-dimensional scene rendering, and the embodiment specifically discloses three-dimensional scene rendering acceleration methods and systems based on video geometric analysis.
Description
Technical Field
The invention relates to the technical field of three-dimensional scene rendering, in particular to three-dimensional scene rendering acceleration methods and systems based on video geometric analysis.
Background
The scene visibility elimination is to eliminate scene parts which do not contribute to final image rendering before a model coordinate conversion stage, and then to send the rest scenes to a rendering pipeline, which can effectively reduce the complexity of the scenes and the burden of a graphic pipeline, and is very effective methods for improving the scene rendering efficiency.
In the fusion visualization process of a multi-source massive real-time monitoring video and a three-dimensional virtual scene of a system , a large amount of time and memory are consumed for large-scale three-dimensional virtual scene rendering and multi-video geometric analysis processing, in a dynamic scene, due to the movement of an object, interaction between an observer and the scene is realized, and a shielding tree corresponding to the scene is changed, if the shielding tree is dynamically generated, the cost is high, and even the drawing time of the whole scene can be exceeded, in the actual application process of the multi-resolution model simplification, a visual jitter phenomenon can occur when a static LOD model is switched between adjacent layers, a dynamic LOD model needs to perform error calculation on vertexes in real time before display, when the data size is large, the calculation amount is huge, the data is required to be completely read into the memory, a large amount of memory can be occupied, preprocessing processes are usually required for model data organization, and the model is more suitable for the static scene.
Disclosure of Invention
In view of the above, the present application provides methods and systems for accelerating three-dimensional scene rendering based on video geometric analysis, which can solve or at least partially solve the above existing problems.
In order to solve the technical problems, the technical scheme provided by the invention is three-dimensional scene rendering acceleration methods based on video geometric analysis, which comprises the following steps:
s1: preprocessing three-dimensional scene data;
s2: constructing a level detail model for the three-dimensional scene data;
s3: and calculating the three-dimensional scene data and the video data by adopting a parallel accelerated calculation method combined with a GPU.
Preferably, the method of step S1 includes:
s11: importing and analyzing scene model objects to be loaded in batch to obtain the central position, the maximum value and the minimum value parameters of a scene model object bounding box;
s12: constructing a tree-shaped index structure for the three-dimensional model with the parameters of the center position, the maximum value and the minimum value of the bounding box, and constructing an R tree for the tree-shaped index structure by adopting a space R tree index method, wherein each node corresponds to a minimum bounding cube containing a corresponding space object;
s13, carrying out space page division on the constructed R tree from leaf nodes to the top by paging technology to obtain independent R trees on each space page, and recording current level information and file direction pointing to a lower layer in the division process;
s14: and sequentially putting the divided R trees into corresponding queues, directly exporting the R trees if the R trees do not contain leaf nodes, and exporting the R trees after performing level of detail LOD processing on the R trees if the R trees contain leaf nodes.
Preferably, the method of step S13 includes:
constructing a visual field range of an observation visual angle, performing intersection operation with a space surrounding cube of the three-dimensional model, acquiring a surrounding cube index positioned in the visual field range through the spatial relationship among nodes, wherein corresponding nodes of a corresponding R tree represent a model part needing rendering and displaying;
and dividing the R tree part corresponding to the selected node through a paging technology, and constructing an independent small R tree by taking the local highest-level non-leaf node as a root node.
Preferably, the method of step S2 includes:
s21, dividing the object needing to construct the level detail model into a plurality of nodes according to the quadtree, wherein the corresponding details exist as lower level nodes on the quadtree of the object surface nodes, and the division of the quadtree determines which level the final rendered leaf nodes are;
s22: performing top point clipping work on the area outside the view volume range, setting the dividing information of the area as false, and setting the dividing information of the leaf node in the view volume range as true;
s23, setting Euclidean distance threshold from an observation point to a specific node and node complexity threshold in a fixed region as evaluation criteria of node segmentation, wherein the distance threshold is set to be 4 grade, and the complexity threshold is judged to be more prioritized than the distance threshold to be grade;
s24, when cracks appear during resolution switching, deleting grid edges on the side of a node with higher resolution level, or adding edges on the side of a node with lower level, merging adjacent grids, and enabling the difference of adjacent resolution levels not to exceed 2 levels;
s25: and performing depth-first traversal on nodes of the currently segmented quadtree by using a recursive method, and rendering all leaf nodes traversed and with the segmentation information of true, thereby completing construction and rendering of a level detail model.
Preferably, the method of step S23 includes:
generating basic model nodes, namely nodes reserved in all observation distances, performing traversal calculation on Euclidean distances from the basic model nodes to the vertex of a visual body of an observation visual angle, setting a distance threshold value to be 4 levels, and increasing the number of the model nodes in a node segmentation mode sequentially along with the distance from a viewpoint to a viewpoint;
and setting a distance weighted average value between model nodes for a more complex model local area, namely a complexity threshold, carrying out local complexity detection while the number of the hierarchical model nodes rises, and determining whether to start a higher complexity model node, wherein the threshold detection has higher priority than the distance threshold.
Preferably, the method of step S3 includes:
s31: preprocessing a three-dimensional scene building and a terrain model object to obtain a large number of small R trees, and loading tree indexes into a GPU (graphics processing unit) for accelerated rendering;
s32: dividing video data into corresponding small blocks in a matrix form according to specific analysis requirements, synchronously loading the small blocks into a GPU (graphics processing unit) for parallel processing, and realizing the operation processing effect accelerated by the GPU;
s33: and combining and outputting the scene data and the video data analysis result to finish the final required rendering effect.
Preferably, the method of step S32 includes:
the method comprises the steps of obtaining video data mapped to the surface of a three-dimensional model in a scene, classifying vertexes at corresponding positions in a vertex shader according to a plurality of planes of the model surface in a mapping range after video textures are rendered and attached to the model surface, completing fragment segmentation in the fragment shader, then performing block parallel computing processing by using a cluster computing unit of a GPU, and synchronously rendering the video textures, so that rendering is accelerated.
The invention also provides three-dimensional scene rendering acceleration systems based on video geometric analysis, which comprises:
the processing module is used for preprocessing the three-dimensional scene data;
constructing a module: constructing a level detail model for the three-dimensional scene data;
and the calculation module is used for calculating the three-dimensional scene data and the video data by adopting a parallel acceleration calculation method combined with the GPU.
The invention also provides three-dimensional scene rendering acceleration systems based on video geometric analysis, which comprises:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the above three-dimensional scene rendering acceleration method based on video geometry analysis.
The present invention further provides computer-readable storage media storing a computer program which, when executed by a processor, implements the steps of the above-mentioned method for accelerating the rendering of a three-dimensional scene based on video geometry analysis.
Compared with the prior art, the method has the advantages that methods for improving video information mapping and model dynamic loading speed in the three-dimensional scene based on the GPU parallel acceleration technology are provided, three-dimensional scene data are preprocessed and index structures are segmented, a hierarchical detail model convenient for accelerated rendering is constructed for the three-dimensional model, a divisible matrix is constructed for the video information, and then the segmented model indexes, the hierarchical detail model and the video image matrix are led into the GPU for parallel calculation, so that accelerated rendering of the complex three-dimensional scene and multiple videos is achieved.
Drawings
While the drawings for illustrating the embodiments of the present invention will be briefly described below for the sake of clarity, it is to be understood that the drawings in the following description are for embodiments of the present invention only, and that other drawings may be derived by those skilled in the art without inventive step.
Fig. 1 is a schematic flowchart of a three-dimensional scene rendering acceleration method based on video geometric analysis according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of preprocessing three-dimensional scene data according to an embodiment of the present invention;
fig. 3 is a schematic flowchart of building a hierarchical detail model for three-dimensional scene data according to an embodiment of the present invention;
fig. 4 is a schematic flowchart of computing three-dimensional scene data and video data by using a parallel accelerated computing method in combination with a GPU according to an embodiment of the present invention;
fig. 5 is a schematic structural diagram of a three-dimensional scene rendering acceleration system based on video geometric analysis according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be described clearly and completely with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only partial embodiments of of the present invention, rather than all embodiments.
In order to make those skilled in the art better understand the technical solution of the present invention, the following detailed description is made with reference to the accompanying drawings and specific embodiments.
As shown in fig. 1, an embodiment of the present invention provides methods for accelerating three-dimensional scene rendering based on video geometric analysis, including:
s1: preprocessing three-dimensional scene data;
s2: constructing a level detail model for the three-dimensional scene data;
s3: and calculating the three-dimensional scene data and the video data by adopting a parallel accelerated calculation method combined with a GPU.
As shown in fig. 2, to solve the problems of high hardware requirement and low rendering efficiency in the prior art, types of model data preprocessing methods suitable for efficient rendering in the urban three-dimensional scene are provided, and a large-scale building model is reorganized, so that a user only needs to load a model in a view volume range when rendering a scene, thereby greatly reducing rendering requirements and improving rendering efficiency, and the specific method of step S1 includes:
s11: and importing and analyzing the scene model objects to be loaded in batch to obtain the central position, the maximum value and the minimum value parameters of the bounding box of the scene model objects.
Specifically, a scene model to be loaded, particularly a large-scale building three-dimensional model with a complex structure, is used as a preprocessing object and is converted into a data format supported by a three-dimensional display platform by using an IO read-write plug-in, so that the requirements of processing and display are met and the data format is imported into a three-dimensional scene in batches; and simultaneously acquiring the central position, the maximum value and the minimum value parameters of the model object bounding box.
S12: and constructing a tree-shaped index structure for the three-dimensional model with the acquired parameters of the center position, the maximum value and the minimum value of the bounding box, and constructing an R tree for the tree-shaped index structure by adopting a space R tree index method, wherein each node corresponds to a minimum bounding cube containing a corresponding space object.
Specifically, a tree index structure is built for a three-dimensional model which is imported and obtains position parameters of a bounding box, an R tree is built by adopting a space R tree index method, wherein each node corresponds to a minimum bounding cube containing a corresponding space object, the specific construction scheme is that minimum bounding cubes containing a space region with a model are used, a building model cluster is taken as an example, the cluster is split into building units of different levels according to the rule of , each split level individual is surrounded by the minimum bounding cube, three non-coplanar vertex coordinates of the cubes are recorded to represent the node index, the coordinate index of the whole bounding cube at the highest level is taken as a root node, the bounding cubes of the minimum units correspond to leaf nodes, all non-leaf node cubes comprise lower-layer cubes, and the rest is done in the same way, and the whole R tree of a regional building is built.
And S13, performing space page division on the constructed R tree from leaf nodes to the top by a paging technology to obtain independent R trees on each space page, and recording current level information and file direction pointing to a lower layer in the division process.
Specifically, the method of step S13 includes:
constructing a visual field range of an observation visual angle, performing intersection operation with a space surrounding cube of the three-dimensional model, acquiring a surrounding cube index positioned in the visual field range through the spatial relationship among nodes, wherein corresponding nodes of a corresponding R tree represent a model part needing rendering and displaying;
and dividing the R tree part corresponding to the selected node through a paging technology, and constructing an independent small R tree by taking the local highest-level non-leaf node as a root node.
The method comprises the specific steps of firstly constructing a visual field range of an observation visual angle, carrying out intersection operation on the visual field range and a spatial surrounding cube of a model, obtaining a surrounding cube index positioned in the visual field range through a spatial relation among nodes, representing a model part needing rendering display corresponding to a corresponding node of the R tree, then dividing the R tree part corresponding to a selected node through a paging technology, and regarding a non-leaf node at the highest level as a root node to construct an independent small R tree.
S14: and sequentially putting the divided R trees into corresponding queues, directly exporting the R trees if the R trees do not contain leaf nodes, and exporting the R trees after performing level of detail LOD processing on the R trees if the R trees contain leaf nodes.
Specifically, a display node queue is constructed for a preselection process of rendering pipeline processing, nodes to be displayed corresponding to a plurality of divided small R tree indexes are sequentially placed in the queue, trees without leaf nodes are directly exported for processing in the next steps, and if the trees contain the leaf nodes, the model corresponding to the R tree surrounding the cube is exported after detail level LOD processing.
As shown in fig. 2, for solving the problems of low direct rendering efficiency, severe fluctuation of frame number when the view angle is moved, and the like caused by increasing the scale of the building model, complex structure, and refining of the DEM in the current three-dimensional scene, a lod (level of detail) technology is used to construct a model with a hierarchical non-equal resolution for the building and the ground surface according to the distance from the view point so as to reduce the rendering overhead and improve the display efficiency, the specific method of step S2 includes:
and S21, dividing the object needing to construct the level detail model into a plurality of nodes according to the quadtree, wherein the corresponding details exist as lower level nodes on the quadtree of the object surface nodes, and the division of the quadtree determines which level the leaf nodes of the final rendering are.
Specifically, after the small R tree corresponding to the three-dimensional scene model object to be rendered (which may be a building or a parcel, etc.) in step S14 is determined and derived, the R tree index including the leaf node is divided into a plurality of levels of nodes according to the quadtree, the corresponding details exist as lower level nodes on the quadtree of the object surface node, and the division result of the quadtree determines which level the leaf node that is finally rendered is located, so that the corresponding model object is constructed into a multilevel detail model according to the observation distance of the viewpoint.
S22: and performing top point clipping work on the area outside the view volume range, setting the splitting information of the area as false, and setting the splitting information of the leaf node in the view volume range as true.
Specifically, the clipping operation of the vertex is performed on the area outside the visual field range, the segmentation discrimination information is set to false, namely, the paging processing is not performed on the corresponding R tree part, and the original whole R tree structure is still reserved, so that the corresponding model node does not participate in the construction queue of the display node, and the effect of not rendering the model outside the visual field range is achieved. The division information of the leaf nodes in the range of the view volume is set to true.
And S23, setting Euclidean distance threshold of the observation point to a specific node and node complexity threshold in a region as evaluation criteria of node segmentation, wherein the distance threshold is set to be 4, and the complexity threshold is judged to be more prioritized than the distance threshold to be .
The method of step S23 includes:
generating basic model nodes, namely nodes reserved in all observation distances, performing traversal calculation on Euclidean distances from the basic model nodes to the vertex of a visual body of an observation visual angle, setting a distance threshold value to be 4 levels, and increasing the number of the model nodes in a node segmentation mode sequentially along with the distance from a viewpoint to a viewpoint;
and setting a distance weighted average value between model nodes for a more complex model local area, namely a complexity threshold, carrying out local complexity detection while the number of the hierarchical model nodes rises, and determining whether to start a higher complexity model node, wherein the threshold detection has higher priority than the distance threshold.
Specifically, a Euclidean distance threshold value from an observation viewpoint to a specific node is set, a node complexity threshold value in a certain area is used as an evaluation standard of node segmentation, the complexity threshold value is judged to be more prioritized than the distance threshold value to be , the method comprises the following specific scheme that basic model nodes are generated firstly, namely nodes reserved in all observation distances are subjected to traversal calculation on the Euclidean distance from the basic model nodes to the vertex of an observation visual angle visual scene, the distance threshold value is set to be 4, the number of model nodes is increased in a node segmentation mode along with the viewpoint from far to near in sequence to improve the model complexity, meanwhile, a weighted average value of the distances between the model nodes is set for a more complicated model local area, namely the complexity threshold value is set, local complexity detection is carried out while the number of the level model nodes is increased, whether the higher-complexity model nodes are started or not is determined, and the threshold value detection is higher than the priority.
S24, when cracks appear during resolution switching, grid edges are deleted on the node side with higher resolution level, or edges are added on the node side with lower level, and adjacent grids are combined, and meanwhile, the difference of adjacent resolution levels does not exceed 2 levels.
Specifically, when a model object with multiple layers of complexity meets a model node switching condition, the problem of local region asynchronism occurs due to observation viewpoint positions, threshold calculation and the like, and therefore splicing cracks occur between models with different complexities, and the problem is solved by selecting to delete grid edges on the model node side with a higher complexity level or add grid edges on the node side with a lower level to merge adjacent grids, wherein the difference of adjacent complexity levels is not more than 2 levels.
S25: and performing depth-first traversal on nodes of the currently segmented quadtree by using a recursive method, and rendering all leaf nodes traversed and with the segmentation information of true, thereby completing construction and rendering of a level detail model.
Specifically, depth-first traversal is performed on the currently segmented quadtree hierarchical nodes by using a recursive method, all leaf nodes which are traversed and whose segmentation information is true, that is, which are in an observation range, are selected, the complexity level of the nodes actually participating in rendering is determined according to the current observation viewpoint, and rendering preparation is performed by deriving the model object corresponding to the quadtree which does not include the leaf node tree in step S14, so that the construction of the hierarchical detail model is completed.
As shown in fig. 4, in step S3, rendering the model object node imported into the rendering queue generated in the previous two steps by using a parallel accelerated computation method combining a GPU and hardware acceleration, so as to achieve a substantial visual effect, which specifically includes:
s31: preprocessing a three-dimensional scene building and a terrain model object to obtain a large number of small R trees, and loading tree indexes into a GPU for accelerated rendering.
Specifically, the segmented R tree indexes of the three-dimensional building model generated in the data preprocessing stage are integrated, the three-dimensional building model is guided into a GPU (graphics processing unit) according to the depth-first traversal order, and the model segmented into a large number of small R tree indexes is subjected to accelerated rendering by means of the GPU.
S32: and dividing the video data into corresponding small blocks in a matrix form according to specific analysis requirements, synchronously loading the small blocks into a GPU (graphics processing unit) for parallel processing, and realizing the operation processing effect accelerated by the GPU.
The method of step S32 includes:
the method comprises the steps of obtaining video data mapped to the surface of a three-dimensional model in a scene, classifying vertexes at corresponding positions in a vertex shader according to a plurality of planes of the model surface in a mapping range after video textures are rendered and attached to the model surface, completing fragment segmentation in the fragment shader, then performing block parallel computing processing by using a cluster computing unit of a GPU, and synchronously rendering the video textures, so that rendering is accelerated.
Specifically, video information accessed from the outside and acquired by a monitoring device or a network is mapped into a three-dimensional scene, is divided into corresponding small blocks in a matrix form according to specific analysis requirements, and is synchronously loaded into a GPU for parallel processing, so that the GPU accelerated operation processing effect is realized. The specific scheme is as follows: video information data mapped to the surface of the three-dimensional model in a scene is obtained, after video textures are rendered and attached to the surface of the model, vertexes at corresponding positions are classified in a vertex shader according to a plurality of planes of the surface of the model in a mapping range, fragment segmentation is completed in a fragment shader, then a cluster computing unit of a GPU is used for conducting block parallel computing processing, the video textures are rendered synchronously, and accordingly rendering is accelerated.
S33: and combining and outputting the scene data and the video data analysis result to finish the final required rendering effect.
Specifically, in a three-dimensional scene, the background, a hierarchical detail model which is accelerated to render by using the GPU and the video texture thereon are output together, so that a final required rendering effect is achieved, and in addition, the application of performing target recognition, tracking monitoring, quantity statistics and other analyses on people, vehicles and other targets in the video can be expanded on the basis of video information.
The three-dimensional scene rendering acceleration method provided by the application is characterized in that in the fusion visualization process of multi-source mass real-time monitoring videos and three-dimensional virtual scenes of a system , the parallel acceleration calculation combined with a GPU is adopted for complex three-dimensional scene rendering and multi-video geometric analysis, the rendering speed and the video geometric analysis speed of the three-dimensional scene are improved under the graphic hardware condition of , and the real-time rendering frame rate is guaranteed.
As shown in fig. 5, an embodiment of the present invention further provides three-dimensional scene rendering acceleration systems based on video geometry analysis, including:
the processing module is used for preprocessing the three-dimensional scene data;
constructing a module: constructing a level detail model for the three-dimensional scene data;
and the calculation module is used for calculating the three-dimensional scene data and the video data by adopting a parallel acceleration calculation method combined with the GPU.
The description of the features of the embodiment corresponding to fig. 5 can refer to the description of the embodiments corresponding to fig. 1 to fig. 4, and is not repeated herein at .
The embodiment of the invention also provides three-dimensional scene rendering acceleration systems based on video geometric analysis, which comprise a memory for storing computer programs and a processor for executing the computer programs to realize the steps of the three-dimensional scene rendering acceleration methods based on video geometric analysis.
An embodiment of the present invention further provides computer-readable storage media, where the computer-readable storage media store computer programs, and when the computer programs are executed by a processor, the computer programs implement the steps of the three-dimensional scene rendering acceleration method based on video geometry analysis as described above.
The three-dimensional scene rendering acceleration methods, systems and computer-readable storage media based on video geometric analysis provided by the embodiments of the present invention are described in detail above, the embodiments in the description are described in a progressive manner, each embodiment focuses on the differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
further appreciate that the various illustrative components and algorithm steps described in connection with the embodiments disclosed herein can be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of functionality for clarity of explanation of interchangeability of hardware and software.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
Claims (10)
1, A three-dimensional scene rendering acceleration method based on video geometric analysis, which is characterized by comprising:
s1: preprocessing three-dimensional scene data;
s2: constructing a level detail model for the three-dimensional scene data;
s3: and calculating the three-dimensional scene data and the video data by adopting a parallel accelerated calculation method combined with a GPU.
2. The method for accelerating rendering of three-dimensional scene based on video geometric analysis according to claim 1, wherein said method of step S1 includes:
s11: importing and analyzing scene model objects to be loaded in batch to obtain the central position, the maximum value and the minimum value parameters of a scene model object bounding box;
s12: constructing a tree-shaped index structure for the three-dimensional model with the parameters of the center position, the maximum value and the minimum value of the bounding box, and constructing an R tree for the tree-shaped index structure by adopting a space R tree index method, wherein each node corresponds to a minimum bounding cube containing a corresponding space object;
s13, carrying out space page division on the constructed R tree from leaf nodes to the top by paging technology to obtain independent R trees on each space page, and recording current level information and file direction pointing to a lower layer in the division process;
s14: and sequentially putting the divided R trees into corresponding queues, directly exporting the R trees if the R trees do not contain leaf nodes, and exporting the R trees after performing level of detail LOD processing on the R trees if the R trees contain leaf nodes.
3. The method for accelerating rendering of three-dimensional scene based on video geometric analysis according to claim 2, wherein said method of step S13 includes:
constructing a visual field range of an observation visual angle, performing intersection operation with a space surrounding cube of the three-dimensional model, acquiring a surrounding cube index positioned in the visual field range through the spatial relationship among nodes, wherein corresponding nodes of a corresponding R tree represent a model part needing rendering and displaying;
and dividing the R tree part corresponding to the selected node through a paging technology, and constructing an independent small R tree by taking the local highest-level non-leaf node as a root node.
4. The method for accelerating rendering of three-dimensional scene based on video geometric analysis according to claim 1, wherein said method of step S2 includes:
s21, dividing the object needing to construct the level detail model into a plurality of nodes according to the quadtree, wherein the corresponding details exist as lower level nodes on the quadtree of the object surface nodes, and the division of the quadtree determines which level the final rendered leaf nodes are;
s22: performing top point clipping work on the area outside the view volume range, setting the dividing information of the area as false, and setting the dividing information of the leaf node in the view volume range as true;
s23, setting Euclidean distance threshold from an observation point to a specific node and node complexity threshold in a fixed region as evaluation criteria of node segmentation, wherein the distance threshold is set to be 4 grade, and the complexity threshold is judged to be more prioritized than the distance threshold to be grade;
s24, when cracks appear during resolution switching, deleting grid edges on the side of a node with higher resolution level, or adding edges on the side of a node with lower level, merging adjacent grids, and enabling the difference of adjacent resolution levels not to exceed 2 levels;
s25: and performing depth-first traversal on nodes of the currently segmented quadtree by using a recursive method, and rendering all leaf nodes traversed and with the segmentation information of true, thereby completing construction and rendering of a level detail model.
5. The method for accelerating rendering of three-dimensional scene based on video geometric analysis according to claim 4, wherein said method of step S23 includes:
generating basic model nodes, namely nodes reserved in all observation distances, performing traversal calculation on Euclidean distances from the basic model nodes to the vertex of a visual body of an observation visual angle, setting a distance threshold value to be 4 levels, and increasing the number of the model nodes in a node segmentation mode sequentially along with the distance from a viewpoint to a viewpoint;
and setting a distance weighted average value between model nodes for a more complex model local area, namely a complexity threshold, carrying out local complexity detection while the number of the hierarchical model nodes rises, and determining whether to start a higher complexity model node, wherein the threshold detection has higher priority than the distance threshold.
6. The method for accelerating rendering of three-dimensional scene based on video geometric analysis according to claim 1, wherein said method of step S3 includes:
s31: preprocessing a three-dimensional scene building and a terrain model object to obtain a large number of small R trees, and loading tree indexes into a GPU (graphics processing unit) for accelerated rendering;
s32: dividing video data into corresponding small blocks in a matrix form according to specific analysis requirements, synchronously loading the small blocks into a GPU (graphics processing unit) for parallel processing, and realizing the operation processing effect accelerated by the GPU;
s33: and combining and outputting the scene data and the video data analysis result to finish the final required rendering effect.
7. The method for accelerating rendering of three-dimensional scene based on video geometric analysis according to claim 6, wherein said method of step S32 includes:
the method comprises the steps of obtaining video data mapped to the surface of a three-dimensional model in a scene, classifying vertexes at corresponding positions in a vertex shader according to a plurality of planes of the model surface in a mapping range after video textures are rendered and attached to the model surface, completing fragment segmentation in the fragment shader, then performing block parallel computing processing by using a cluster computing unit of a GPU, and synchronously rendering the video textures, so that rendering is accelerated.
8, A three-dimensional scene rendering acceleration system based on video geometric analysis, comprising:
the processing module is used for preprocessing the three-dimensional scene data;
constructing a module: constructing a level detail model for the three-dimensional scene data;
and the calculation module is used for calculating the three-dimensional scene data and the video data by adopting a parallel acceleration calculation method combined with the GPU.
9, A three-dimensional scene rendering acceleration system based on video geometric analysis, comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the method for accelerating the rendering of a three-dimensional scene based on video geometry analysis according to any of claims 1 to 7.
10, computer-readable storage medium, characterized in that the computer-readable storage medium stores a computer program which, when being executed by a processor, realizes the steps of the method for accelerating the rendering of a three-dimensional scene based on video geometry analysis according to any of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910969273.XA CN110738721B (en) | 2019-10-12 | 2019-10-12 | Three-dimensional scene rendering acceleration method and system based on video geometric analysis |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910969273.XA CN110738721B (en) | 2019-10-12 | 2019-10-12 | Three-dimensional scene rendering acceleration method and system based on video geometric analysis |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN110738721A true CN110738721A (en) | 2020-01-31 |
| CN110738721B CN110738721B (en) | 2023-09-01 |
Family
ID=69268830
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910969273.XA Active CN110738721B (en) | 2019-10-12 | 2019-10-12 | Three-dimensional scene rendering acceleration method and system based on video geometric analysis |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110738721B (en) |
Cited By (20)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111402382A (en) * | 2020-03-18 | 2020-07-10 | 东南数字经济发展研究院 | Classification optimization method for improving data rendering efficiency of layered and partitioned three-dimensional model |
| CN111563948A (en) * | 2020-03-30 | 2020-08-21 | 南京舆图科技发展有限公司 | Virtual terrain rendering method for dynamically processing and caching resources based on GPU |
| CN111899585A (en) * | 2020-07-23 | 2020-11-06 | 国网上海市电力公司 | Simulation training system and method for manufacturing cable accessories |
| CN112070909A (en) * | 2020-09-02 | 2020-12-11 | 中国石油工程建设有限公司 | Engineering three-dimensional model LOD output method based on 3D Tiles |
| CN112215935A (en) * | 2020-12-02 | 2021-01-12 | 江西博微新技术有限公司 | LOD model automatic switching method and device, electronic equipment and storage medium |
| CN112231020A (en) * | 2020-12-16 | 2021-01-15 | 成都完美时空网络技术有限公司 | Model switching method and device, electronic equipment and storage medium |
| CN113342999A (en) * | 2021-05-07 | 2021-09-03 | 上海大学 | Variable-resolution-ratio point cloud simplification method based on multi-layer skip sequence tree structure |
| CN114359500A (en) * | 2022-03-10 | 2022-04-15 | 西南交通大学 | Three-dimensional modeling and visualization method for landslide hazard range prediction |
| CN114463473A (en) * | 2020-11-09 | 2022-05-10 | 中兴通讯股份有限公司 | Image rendering processing method and device, storage medium and electronic equipment |
| CN114581573A (en) * | 2021-12-13 | 2022-06-03 | 北京市建筑设计研究院有限公司 | Local rendering method and device of three-dimensional scene, electronic equipment and storage medium |
| CN115311412A (en) * | 2022-08-09 | 2022-11-08 | 北京飞渡科技有限公司 | Load-balanced large-volume three-dimensional scene LOD construction method and device and electronic equipment |
| CN115661327A (en) * | 2022-12-09 | 2023-01-31 | 北京盈建科软件股份有限公司 | Distributed virtual node rendering method and device of BIM (building information modeling) platform graphic engine |
| WO2023066122A1 (en) * | 2021-10-22 | 2023-04-27 | 华为技术有限公司 | Three-dimensional model data processing method, three-dimensional model data generation method, and related apparatuses |
| CN116109750A (en) * | 2023-02-28 | 2023-05-12 | 北京达美盛软件股份有限公司 | Three-dimensional model rendering system and method |
| CN116309974A (en) * | 2022-12-21 | 2023-06-23 | 四川聚川诚名网络科技有限公司 | Animation scene rendering method, system, electronic equipment and medium |
| CN116433821A (en) * | 2023-04-17 | 2023-07-14 | 上海臻图信息技术有限公司 | Three-dimensional model rendering method, medium and device for pre-generating view point index |
| CN116740249A (en) * | 2023-08-15 | 2023-09-12 | 湖南马栏山视频先进技术研究院有限公司 | Distributed three-dimensional scene rendering system |
| CN117611472A (en) * | 2024-01-24 | 2024-02-27 | 四川物通科技有限公司 | Fusion method for metaspace and cloud rendering |
| CN117808949A (en) * | 2023-12-29 | 2024-04-02 | 中数科技(青岛)有限公司 | Scene rendering method |
| CN117953181A (en) * | 2024-03-27 | 2024-04-30 | 江苏狄诺尼信息技术有限责任公司 | Vertex layering and incremental LOD method and system for WEB3D |
Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090027402A1 (en) * | 2003-11-19 | 2009-01-29 | Lucid Information Technology, Ltd. | Method of controlling the mode of parallel operation of a multi-mode parallel graphics processing system (MMPGPS) embodied within a host comuting system |
| WO2012083508A1 (en) * | 2010-12-24 | 2012-06-28 | 中国科学院自动化研究所 | Fast rendering method of third dimension of complex scenes in internet |
| CN102867331A (en) * | 2012-08-31 | 2013-01-09 | 电子科技大学 | Graphics processing unit (GPU)-orientated large-scale terrain fast drawing method |
| CN103268342A (en) * | 2013-05-21 | 2013-08-28 | 北京大学 | DEM dynamic visualization accelerating system and method based on CUDA |
| US20130335445A1 (en) * | 2012-06-18 | 2013-12-19 | Xerox Corporation | Methods and systems for realistic rendering of digital objects in augmented reality |
| US8913068B1 (en) * | 2011-07-12 | 2014-12-16 | Google Inc. | Displaying video on a browser |
| CN104751505A (en) * | 2013-06-19 | 2015-07-01 | 国家电网公司 | Three-dimensional scene rendering algorithm based on LOD (Levels of Detail) model and quadtree level structure |
| US9330486B1 (en) * | 2012-08-07 | 2016-05-03 | Lockheed Martin Corporation | Optimizations of three-dimensional (3D) geometry |
| CN105957149A (en) * | 2016-05-31 | 2016-09-21 | 浙江科澜信息技术有限公司 | Urban three-dimensional model data preprocessing method suitable for high-efficiency rendering |
| CN106027855A (en) * | 2016-05-16 | 2016-10-12 | 深圳迪乐普数码科技有限公司 | Method and terminal for realizing virtual rocker arm |
| US20170039765A1 (en) * | 2014-05-05 | 2017-02-09 | Avigilon Fortress Corporation | System and method for real-time overlay of map features onto a video feed |
| CN107340501A (en) * | 2017-07-02 | 2017-11-10 | 中国航空工业集团公司雷华电子技术研究所 | Radar video method of processing display based on OpenGL ES |
| CN107835436A (en) * | 2017-09-25 | 2018-03-23 | 北京航空航天大学 | A kind of real-time virtual reality fusion live broadcast system and method based on WebGL |
| CN107886564A (en) * | 2017-10-13 | 2018-04-06 | 上海秉匠信息科技有限公司 | The method shown for realizing three-dimensional scenic |
| CN109945817A (en) * | 2019-05-07 | 2019-06-28 | 四川航天神坤科技有限公司 | One population Molded Depth degree measuring device |
| US20190235917A1 (en) * | 2015-05-26 | 2019-08-01 | Thincl, Inc. | Configurable scheduler in a graph streaming processing system |
| CN110084739A (en) * | 2019-03-28 | 2019-08-02 | 东南大学 | A kind of parallel acceleration system of FPGA of the picture quality enhancement algorithm based on CNN |
-
2019
- 2019-10-12 CN CN201910969273.XA patent/CN110738721B/en active Active
Patent Citations (17)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20090027402A1 (en) * | 2003-11-19 | 2009-01-29 | Lucid Information Technology, Ltd. | Method of controlling the mode of parallel operation of a multi-mode parallel graphics processing system (MMPGPS) embodied within a host comuting system |
| WO2012083508A1 (en) * | 2010-12-24 | 2012-06-28 | 中国科学院自动化研究所 | Fast rendering method of third dimension of complex scenes in internet |
| US8913068B1 (en) * | 2011-07-12 | 2014-12-16 | Google Inc. | Displaying video on a browser |
| US20130335445A1 (en) * | 2012-06-18 | 2013-12-19 | Xerox Corporation | Methods and systems for realistic rendering of digital objects in augmented reality |
| US9330486B1 (en) * | 2012-08-07 | 2016-05-03 | Lockheed Martin Corporation | Optimizations of three-dimensional (3D) geometry |
| CN102867331A (en) * | 2012-08-31 | 2013-01-09 | 电子科技大学 | Graphics processing unit (GPU)-orientated large-scale terrain fast drawing method |
| CN103268342A (en) * | 2013-05-21 | 2013-08-28 | 北京大学 | DEM dynamic visualization accelerating system and method based on CUDA |
| CN104751505A (en) * | 2013-06-19 | 2015-07-01 | 国家电网公司 | Three-dimensional scene rendering algorithm based on LOD (Levels of Detail) model and quadtree level structure |
| US20170039765A1 (en) * | 2014-05-05 | 2017-02-09 | Avigilon Fortress Corporation | System and method for real-time overlay of map features onto a video feed |
| US20190235917A1 (en) * | 2015-05-26 | 2019-08-01 | Thincl, Inc. | Configurable scheduler in a graph streaming processing system |
| CN106027855A (en) * | 2016-05-16 | 2016-10-12 | 深圳迪乐普数码科技有限公司 | Method and terminal for realizing virtual rocker arm |
| CN105957149A (en) * | 2016-05-31 | 2016-09-21 | 浙江科澜信息技术有限公司 | Urban three-dimensional model data preprocessing method suitable for high-efficiency rendering |
| CN107340501A (en) * | 2017-07-02 | 2017-11-10 | 中国航空工业集团公司雷华电子技术研究所 | Radar video method of processing display based on OpenGL ES |
| CN107835436A (en) * | 2017-09-25 | 2018-03-23 | 北京航空航天大学 | A kind of real-time virtual reality fusion live broadcast system and method based on WebGL |
| CN107886564A (en) * | 2017-10-13 | 2018-04-06 | 上海秉匠信息科技有限公司 | The method shown for realizing three-dimensional scenic |
| CN110084739A (en) * | 2019-03-28 | 2019-08-02 | 东南大学 | A kind of parallel acceleration system of FPGA of the picture quality enhancement algorithm based on CNN |
| CN109945817A (en) * | 2019-05-07 | 2019-06-28 | 四川航天神坤科技有限公司 | One population Molded Depth degree measuring device |
Non-Patent Citations (2)
| Title |
|---|
| K. BENKRID;D. CROOKES;J. SMITH;A. BENKRID: "High level programming for real time FPGA based video processing", 《2000 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH, AND SIGNAL PROCESSING》 * |
| 罗冠,郝重阳,淮永建,张先勇,高晓滨: "虚拟现实引擎的设计与实现", 《计算机学报》 * |
Cited By (30)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111402382B (en) * | 2020-03-18 | 2023-04-07 | 东南数字经济发展研究院 | Classification optimization method for improving data rendering efficiency of layered and partitioned three-dimensional model |
| CN111402382A (en) * | 2020-03-18 | 2020-07-10 | 东南数字经济发展研究院 | Classification optimization method for improving data rendering efficiency of layered and partitioned three-dimensional model |
| CN111563948A (en) * | 2020-03-30 | 2020-08-21 | 南京舆图科技发展有限公司 | Virtual terrain rendering method for dynamically processing and caching resources based on GPU |
| CN111899585A (en) * | 2020-07-23 | 2020-11-06 | 国网上海市电力公司 | Simulation training system and method for manufacturing cable accessories |
| CN112070909A (en) * | 2020-09-02 | 2020-12-11 | 中国石油工程建设有限公司 | Engineering three-dimensional model LOD output method based on 3D Tiles |
| CN112070909B (en) * | 2020-09-02 | 2024-06-11 | 中国石油工程建设有限公司 | Engineering three-dimensional model LOD output method based on 3D Tiles |
| CN114463473A (en) * | 2020-11-09 | 2022-05-10 | 中兴通讯股份有限公司 | Image rendering processing method and device, storage medium and electronic equipment |
| WO2022095714A1 (en) * | 2020-11-09 | 2022-05-12 | 中兴通讯股份有限公司 | Image rendering processing method and apparatus, storage medium, and electronic device |
| CN112215935A (en) * | 2020-12-02 | 2021-01-12 | 江西博微新技术有限公司 | LOD model automatic switching method and device, electronic equipment and storage medium |
| CN112231020A (en) * | 2020-12-16 | 2021-01-15 | 成都完美时空网络技术有限公司 | Model switching method and device, electronic equipment and storage medium |
| CN113342999B (en) * | 2021-05-07 | 2022-08-05 | 上海大学 | Variable-resolution-ratio point cloud simplification method based on multi-layer skip sequence tree structure |
| CN113342999A (en) * | 2021-05-07 | 2021-09-03 | 上海大学 | Variable-resolution-ratio point cloud simplification method based on multi-layer skip sequence tree structure |
| WO2023066122A1 (en) * | 2021-10-22 | 2023-04-27 | 华为技术有限公司 | Three-dimensional model data processing method, three-dimensional model data generation method, and related apparatuses |
| CN114581573A (en) * | 2021-12-13 | 2022-06-03 | 北京市建筑设计研究院有限公司 | Local rendering method and device of three-dimensional scene, electronic equipment and storage medium |
| CN114359500A (en) * | 2022-03-10 | 2022-04-15 | 西南交通大学 | Three-dimensional modeling and visualization method for landslide hazard range prediction |
| CN114359500B (en) * | 2022-03-10 | 2022-05-24 | 西南交通大学 | Three-dimensional modeling and visualization method for landslide hazard range prediction |
| CN115311412A (en) * | 2022-08-09 | 2022-11-08 | 北京飞渡科技有限公司 | Load-balanced large-volume three-dimensional scene LOD construction method and device and electronic equipment |
| CN115661327A (en) * | 2022-12-09 | 2023-01-31 | 北京盈建科软件股份有限公司 | Distributed virtual node rendering method and device of BIM (building information modeling) platform graphic engine |
| CN115661327B (en) * | 2022-12-09 | 2023-05-30 | 北京盈建科软件股份有限公司 | Distributed virtual node rendering method and device of BIM platform graphic engine |
| CN116309974A (en) * | 2022-12-21 | 2023-06-23 | 四川聚川诚名网络科技有限公司 | Animation scene rendering method, system, electronic equipment and medium |
| CN116309974B (en) * | 2022-12-21 | 2023-11-28 | 四川聚川诚名网络科技有限公司 | Animation scene rendering method, system, electronic equipment and medium |
| CN116109750A (en) * | 2023-02-28 | 2023-05-12 | 北京达美盛软件股份有限公司 | Three-dimensional model rendering system and method |
| CN116433821A (en) * | 2023-04-17 | 2023-07-14 | 上海臻图信息技术有限公司 | Three-dimensional model rendering method, medium and device for pre-generating view point index |
| CN116433821B (en) * | 2023-04-17 | 2024-01-23 | 上海臻图信息技术有限公司 | Three-dimensional model rendering method, medium and device for pre-generating view point index |
| CN116740249A (en) * | 2023-08-15 | 2023-09-12 | 湖南马栏山视频先进技术研究院有限公司 | Distributed three-dimensional scene rendering system |
| CN116740249B (en) * | 2023-08-15 | 2023-10-27 | 湖南马栏山视频先进技术研究院有限公司 | Distributed three-dimensional scene rendering system |
| CN117808949A (en) * | 2023-12-29 | 2024-04-02 | 中数科技(青岛)有限公司 | Scene rendering method |
| CN117611472A (en) * | 2024-01-24 | 2024-02-27 | 四川物通科技有限公司 | Fusion method for metaspace and cloud rendering |
| CN117611472B (en) * | 2024-01-24 | 2024-04-09 | 四川物通科技有限公司 | Fusion method for metaspace and cloud rendering |
| CN117953181A (en) * | 2024-03-27 | 2024-04-30 | 江苏狄诺尼信息技术有限责任公司 | Vertex layering and incremental LOD method and system for WEB3D |
Also Published As
| Publication number | Publication date |
|---|---|
| CN110738721B (en) | 2023-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110738721B (en) | Three-dimensional scene rendering acceleration method and system based on video geometric analysis | |
| CN110070613B (en) | Large three-dimensional scene webpage display method based on model compression and asynchronous loading | |
| EP3882865A1 (en) | Object loading method, device, storage medium, and electronic device | |
| CN102157008B (en) | Large-scale virtual crowd real-time rendering method | |
| US8570322B2 (en) | Method, system, and computer program product for efficient ray tracing of micropolygon geometry | |
| CN101241603B (en) | Electromagnetic field intensity real time visualized method | |
| CN113034656B (en) | Rendering method, device and equipment for illumination information in game scene | |
| CN113034657B (en) | Rendering method, device and equipment for illumination information in game scene | |
| WO2012037863A1 (en) | Method for simplifying and progressively transmitting 3d model data and device therefor | |
| JP2009525526A (en) | Method for synthesizing virtual images by beam emission | |
| CN111145356B (en) | Cutting method based on Unity3D model | |
| CN112785673B (en) | Rendering method based on WebGL batch three-dimensional model data | |
| CN102831634B (en) | Efficient accurate general soft shadow generation method | |
| CN110935171A (en) | Method for loading, optimizing and unitizing live-action three-dimensional model in game engine | |
| CN112070909A (en) | Engineering three-dimensional model LOD output method based on 3D Tiles | |
| CN117237503B (en) | Geographic element data accelerated rendering and device | |
| CN110738719A (en) | Web3D model rendering method based on visual range hierarchical optimization | |
| Lee et al. | A bimodal empty space skipping of ray casting for terrain data | |
| CN116958457A (en) | OSGEarth-based war misting effect drawing method | |
| JP2015022761A (en) | Image processing apparatus and method | |
| CN115035231A (en) | Shadow baking method, shadow baking device, electronic apparatus, and storage medium | |
| Bao et al. | Billboards for tree simplification and real-time forest rendering | |
| CN111354081A (en) | Large-scale three-dimensional forest drawing method | |
| CN115423917B (en) | Real-time drawing method and system for global three-dimensional wind field | |
| KR100726031B1 (en) | A terrain rendering method using a cube mesh structure |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |