CN110070613B - Large three-dimensional scene webpage display method based on model compression and asynchronous loading - Google Patents
Large three-dimensional scene webpage display method based on model compression and asynchronous loading Download PDFInfo
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Abstract
The invention provides a large three-dimensional scene webpage display method based on model compression and asynchronous loading, and relates to the technical field of computer graphics. The invention comprises the following steps: step 1: acquiring a two-dimensional picture of a scene to be reconstructed, and generating a three-dimensional model of the scene to be reconstructed by using a three-dimensional reconstruction technology; step 2: dividing a point cloud space in the model into small cubes according to the three-dimensional model, and forming structured data by using octree; and step 3: calculating the node size and the depth of the octree model; and 4, step 4: constructing an octree model; and 5: compressing an octree model requested by a user at a server side, and transmitting the octree model to a browser for rendering and displaying through a network; step 6: and dynamically loading nodes of the octree model on the webpage according to the level detail technology, and rendering the nodes to finally obtain the three-dimensional scene graph of the scene to be reconstructed. The method ensures the real-time performance of model rendering in webpage model display and greatly shortens the waiting time of a user.
Description
Technical Field
The invention relates to the technical field of computer graphics, in particular to a large three-dimensional scene webpage display method based on model compression and asynchronous loading.
Background
The real world is three-dimensional, human visual perception is three-dimensional, and three-dimensional models represent objects in the real world more realistically, abundantly, and vividly than textual information and two-dimensional images. Currently, a large three-dimensional model is displayed by some commercialized software, but the large three-dimensional model is expensive and mostly developed based on a client. The use of the client application requires relatively complicated steps such as downloading, installation, updating, and the like. And because the data needs to be stored locally, the requirement on the client is high when a large amount of data is processed, and meanwhile, the portability of the application program of the client is poor due to the synchronization problem of the data. In addition, the client application is usually developed based on a certain specific platform, and has no portability.
With the advent of WebGL and other technologies, three-dimensional models can also be displayed on web pages. The model display is directly carried out for the user in the browser, so that the installation of additional software can be avoided, the lightweight client side is realized, and the portability is also improved. Meanwhile, HTML, CSS and JavaScript scripting languages have the advantages of being concise, good in expansibility and the like, the development process of Web application is accelerated, multiple development on different platforms is avoided, and convenience is brought to practical application and popularization of the three-dimensional model.
However, due to the limitation of network transmission and the requirement of rendering real-time performance when interacting with users, the display of large three-dimensional models on web pages has many difficulties. The data volume of a high-precision large three-dimensional model is very large, the three-dimensional model which directly shows the large data volume through a webpage has the problems that the memory consumption is serious, reasonable release cannot be realized, the blockage is obvious, the visual angle cannot be smoothly switched, and the like, and good user interaction experience is difficult to achieve. Therefore, complex scene management needs to be performed, for example, technologies such as model compression, model simplification, asynchronous loading, hierarchical detail display, and the like are used to reduce the transmission amount of the network as much as possible, reduce the time for blocking and waiting for the user, and achieve good user interaction. The current common scene management method is to divide point cloud spatially by using octree, quadtree, KD tree or R tree, and then to build a multi-resolution point cloud structure by using the idea of hierarchical level detail display to realize the improvement of rendering efficiency. In addition, in the aspect of rendering authenticity, a file of the model usually contains coordinate information and material information of the point cloud, the WebGL provides a relatively convenient API for the graphic rendering of the Web end, and a graphic rendering framework based on the WebGL and HTML covers some complex details of the WebGL, such as ThreeJs and the like, so that the three-dimensional rendering of the Web end is facilitated. However, the large three-dimensional model at the Web end still has the problems of slow loading, slow jamming and the like, and the rendering efficiency of the model and the interaction of the user have great promotion space.
Disclosure of Invention
The technical problem to be solved by the invention is to provide a large three-dimensional scene webpage display method based on model compression and asynchronous loading aiming at the defects of the prior art, so that the real-time performance of model rendering in webpage model display is ensured, and the waiting time of a user is greatly shortened.
In order to solve the technical problems, the technical scheme adopted by the invention is as follows:
the invention provides a large three-dimensional scene webpage display method based on model compression and asynchronous loading, which comprises the following steps:
step 1: acquiring a two-dimensional picture of a scene to be reconstructed, and generating a three-dimensional model of the scene to be reconstructed by using a three-dimensional reconstruction technology;
and 2, step: dividing a point cloud space in the model into small cubes according to the three-dimensional model obtained in the step 1, and forming structured data by using an octree;
step 2.1: calculating a cube bounding box B capable of containing the whole scene according to the point cloud geometrical structure of the whole three-dimensional model, gradually dividing the cube into small cubes, and stopping continuously dividing when a termination condition is reached in the dividing process to finish the division of the point cloud space; the termination condition is that the volume of the divided small cube is at least one point;
the termination condition is that the volume of the divided small cube at least can contain one point; the minimum coverage area r of each point is determined, and the calculation method of r is as follows: calculating the mean value mu and the variance sigma of the distance between each adjacent point in the point cloud; and obtaining the minimum coverage range r of each point in the point cloud according to the mean value mu and the variance sigma, wherein the formula is as follows: r = μ + σ;
step 2.2: an octree structure is used as an organization mode of data, each cube comprises S points, and S is a positive integer; each cube can be used as a leaf node of an octree to store the coordinate information and the color of the corresponding point;
and step 3: calculating the node size and depth of the octree model;
when the size of the node file is reduced, the load of the memory is reduced, the depth of the octree is deepened when the load is reduced, when the size of the node file is increased, the load of the memory is increased, and the depth of the octree is shallower when the load is increased;
the calculation formula of the leaf node size Y is as follows:
wherein x, y and z respectively represent the length, width and height of the bounding box B; n is the depth of the octree; setting the value of leaf node size Y according to the limit conditions of different computer memories, thereby calculating the minimum value of N according to the formula, and determining the point cloud range covered by the node on each level of the octree according to the depth N of the octree;
and 4, step 4: constructing an octree model;
according to sampling density D, points in three-dimensional model point cloud n Sampling to generate a root node, dividing the rest nodes into eight regions according to spatial positions, respectively sampling points in the eight regions in a parallel mode, and taking point clouds obtained by sampling as eight child nodes of the root node; recursion is carried out, the recursion is stopped until the number of the points in the nodes is smaller than a preset threshold value lambda or the depth of the octree reaches a preset depth N, and an octree model is output; setting the threshold lambda as the size range of the leaf node file which is artificially set after balancing the load of the memory and the rendering efficiency in the step 3;
sampling density D of nodes in each layer n The calculation formula of (c) is:
D n ＝2 N-1-n *r
wherein n represents the number of layers of the octree at present;
and 5: compressing an octree model requested by a user at a server side by using a website compression technology, and transmitting compressed data to a browser through a network for rendering and displaying;
and 6: and dynamically loading nodes of the octree model on the webpage according to the level detail technology, and rendering the nodes to finally obtain the three-dimensional scene graph of the scene to be reconstructed.
The specific steps of the step 6 are as follows:
step 6.1: loading data of a root node in the octree model, setting weights of the nodes according to the size of the area of the bounding box projected to a screen at the server side, wherein the larger the area is, the larger the weight is, then generating a rendering queue according to the weights of the nodes, arranging the nodes with the larger weights in front of the rendering queue for rendering preferentially,
step 6.2: updating the rendering queue when the viewpoint changes or zooming-in and zooming-out operations are carried out;
setting two queues, wherein the rendering queue is used for storing nodes which are loaded to the local and wait for rendering, and the cache queue is used for storing nodes which are not loaded to the local in the cache; when a user moves the visual angle of the three-dimensional model, traversing the nodes of the octree layer by layer in a breadth-first mode, and performing occlusion rejection and rendering queue updating according to the visibility of the octree nodes at the current visual angle, namely removing occluded points from a rendering queue when part of point clouds stored in the octree nodes at the current visual angle are in an occluded state; when the viewpoint changes or the zooming-in and zooming-out operations are carried out, adding nodes which are not loaded to the local in the nodes of the current layer into a priority queue of a cache module, and carrying out asynchronous dynamic loading preferentially;
step 6.3: after the asynchronous node data loading is finished, judging whether the cache exceeds a preset threshold value beta, and if so, deleting nodes which are not used for a long time from the cache; the threshold beta is set according to the cache condition of the computer;
adopt the produced beneficial effect of above-mentioned technical scheme to lie in: according to the large three-dimensional scene webpage display method based on model compression and asynchronous loading, the three-dimensional scene obtained through reconstruction is generally complex point clouds with dense spatial distribution, the data volume of the point clouds is large, and the point clouds can be displayed on the webpage only through optimization processing. Therefore, the invention uses the octree method and combines the level detail technology to organize and manage the point cloud, namely, the asynchronous loading technology is used to carry out hierarchical refinement on the model according to the distance between the visual angle and the viewpoint and the model, the scene management of the multi-resolution structure is realized, and the webpage display of the large three-dimensional model is realized by combining a certain memory management strategy, so that the large three-dimensional model can be displayed on the webpage with the display effect as shown in figure 4, and the loading speed of the model and the fluency of user interaction are improved.
Drawings
FIG. 1 is a flow chart of a method provided by an embodiment of the present invention;
FIG. 2 is a schematic diagram of space division of an octree according to an embodiment of the present invention;
fig. 3 is a flowchart of dynamic node loading and memory management according to an embodiment of the present invention;
FIG. 4 is a representation of a large three-dimensional scene provided by an embodiment of the present invention;
FIG. 5 is a display using sampled point clouds when the model is located a large distance from the viewpoint according to an embodiment of the present invention;
FIG. 6 is a point cloud presentation diagram illustrating a finer use of the model and the viewpoint distance;
Detailed Description
The following detailed description of the present invention is provided in connection with the accompanying drawings and examples. The following examples are intended to illustrate the invention but are not intended to limit the scope of the invention.
As shown in fig. 1, the method of the present embodiment is as follows.
The invention provides a large three-dimensional scene webpage display method based on model compression and asynchronous loading, which comprises the following steps:
step 1: acquiring a two-dimensional picture of a scene to be reconstructed, and generating a three-dimensional model of the scene to be reconstructed by using a three-dimensional reconstruction technology;
and 2, step: dividing a point cloud space in the model into small cubes according to the three-dimensional model obtained in the step 1, and forming structured data by using an octree;
step 2.1: calculating a cube bounding box B capable of containing the whole scene according to the point cloud geometrical structure of the whole three-dimensional model, gradually dividing the cube into small cubes, and stopping continuously dividing when a termination condition is reached in the dividing process to finish the division of the point cloud space; the termination condition is that the volume of the divided small cube at least can contain one point;
step 2.2: an octree structure is used as a data organization mode, each cube comprises S points, and S is a positive integer; each small cube is used as a leaf node of the octree to store the coordinate information and the color of the corresponding point;
due to different scenes, the densities of the points in the reconstructed model point cloud are different, the mean value mu and the variance sigma of the distances between adjacent points are calculated by utilizing the coordinates of the points to obtain the minimum coverage range r of each point in the point cloud, wherein the minimum coverage range r is represented as r = mu + sigma, at the moment, the r determines the termination condition of dividing the cube, namely the volume of the divided cube at least can contain one point, otherwise, the continuous division is stopped.
And 3, step 3: calculating the node size and depth of the octree model;
setting the node size of each node in the octree and the depth of the octree; when the size of the node file is reduced, the load of the memory is reduced, the depth of the octree is deepened when the load is reduced, when the size of the node file is increased, the load of the memory is increased, and the depth of the octree is shallower when the load is increased.
The calculation formula of the leaf node size Y is as follows:
wherein x, y and z respectively represent the length, width and height of the bounding box B; n is the depth of the octree; setting the value of leaf node size Y according to the limit conditions of different computer memories, thereby calculating the minimum value of N according to the formula, and determining the point cloud range covered by the node on each level of the octree according to the depth N of the octree; knowing the depth of the octree, the leaf node number of the octree is known, and the leaf node number of the octree is equal to the number of the divided small cubes, and each small cube after division comprises a plurality of points, namely the coverage range of the small cube.
And 4, step 4: constructing an octree model (from top to bottom, thinning layer by layer); as shown in fig. 2;
sampling points in the three-dimensional model point cloud to generate a root node, dividing the rest nodes into eight regions according to spatial positions, respectively sampling the points in the eight regions in a parallel mode, and taking the sampled point cloud as eight child nodes of the root node. Constructing the sub nodes of the octree in a recursion manner, stopping the recursion until the number of points in the nodes (the number of points in the point cloud contained in the nodes) is smaller than a preset threshold value lambda or the depth of the octree reaches a preset depth N, and outputting an octree model; setting the threshold lambda according to the size range of the leaf node file set artificially after balancing the load of the memory and the rendering efficiency in the step 3;
per layer node sampling density D n The calculation formula of (c) is:
D n ＝2 N-1-n *r
wherein n represents the number of layers of the octree at present;
and 5: compressing an octree model requested by a user at a server side by using a website compression technology, and transmitting compressed data to a browser through a network for rendering and displaying; after the scene organization management, namely the steps 1 to 4 are the steps of managing the scene organization; in order to further reduce the time consumption of network transmission, the waiting time of users is shortened. On the premise of ensuring the model display effect, the invention firstly compresses the data to be transmitted at the server end, and then transmits the compressed data to the browser for rendering display through the network.
And 6: dynamically loading nodes of the octree model on a webpage according to a level detail technology, and rendering the nodes to finally obtain a three-dimensional scene graph of a scene to be reconstructed;
step 6.1: loading data of a root node in the octree model, setting weights of the nodes according to the size of an area projected by the bounding box onto a screen at the server side, wherein the weights are larger when the area is larger, then generating a rendering queue according to the weights of the nodes, arranging the nodes with the larger weights in front of the rendering queue for rendering preferentially,
step 6.2: updating the rendering queue when the viewpoint changes or zooming-in and zooming-out operations are carried out;
setting two queues, wherein the rendering queue is used for storing nodes which are loaded to the local and wait for rendering, and the cache queue is used for storing nodes which are not loaded to the local in the cache; when a user moves the visual angle of the three-dimensional model, traversing the nodes of the octree layer by layer in a breadth-first mode, and performing occlusion rejection and rendering queue updating according to the visibility of the octree nodes under the current visual angle; namely, under the current view angle, part of point clouds stored in octree nodes are in a shielded state, and the shielded points are removed from a rendering queue; when the viewpoint changes or the zooming-in and zooming-out operations are carried out, adding the nodes which are not loaded to the local in the nodes of the current layer into a priority queue of a cache module, and preferentially carrying out asynchronous dynamic loading;
in this embodiment, when the model is enlarged and reduced, the rendering effect of the point cloud is as shown in fig. 5 and 6, and fig. 5 is a point cloud presentation graph using sampling when the distance between the model and the viewpoint is large; FIG. 6 is a diagram showing a point cloud with a finer use of the model and the closer viewpoint;
step 6.3: after the asynchronous node data loading is finished, judging whether the cache exceeds a preset threshold value beta, and if so, deleting nodes which are not used for a long time from the cache; the threshold value beta is set according to the cache condition of the computer, and is generally set to be 80% of the cache;
the processing logic for dynamic loading and memory management is shown in fig. 3.
In addition, due to the idea of combining octrees with hierarchical details, octree models with different hierarchical details should be loaded when view angle conversion or zooming-in and zooming-out operations are performed on the models. Therefore, weights need to be calculated for nodes of the octree, and then a rendering queue is generated according to the weights of the nodes, so that a smoother rendering result can be obtained when the view angle conversion and the model zooming-in and zooming-out operations are performed. The weight of the node is determined according to the size of the area of the node bounding box projected onto a screen under the current visual angle.
Finally, it should be noted that: the above examples are only intended to illustrate the technical solution of the present invention, and not to limit it; although the present invention has been described in detail with reference to the foregoing embodiments, it should be understood by those of ordinary skill in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; such modifications or substitutions do not depart from the spirit of the invention, which is defined by the claims.
Claims (2)
1. A large three-dimensional scene webpage display method based on model compression and asynchronous loading is characterized in that: the method comprises the following steps:
step 1: acquiring a two-dimensional picture of a scene to be reconstructed, and generating a three-dimensional model of the scene to be reconstructed by using a three-dimensional reconstruction technology;
step 2: dividing a point cloud space in the model into small cubes according to the three-dimensional model obtained in the step 1, and forming structured data by using an octree;
step 2.1: calculating a cube bounding box B capable of containing the whole scene according to the point cloud geometrical structure of the whole three-dimensional model, gradually dividing the cube into small cubes, and stopping continuously dividing when a termination condition is reached in the dividing process to finish the division of the point cloud space; the termination condition is that the volume of the divided small cube at least comprises one point;
the termination condition is that the volume of the divided small cube at least can contain one point; the minimum coverage range r of each point is determined, and the calculation method of r is as follows: calculating the mean value mu and the variance sigma of the distance between every two adjacent points in the point cloud; and obtaining the minimum coverage range r of each point in the point cloud according to the mean value mu and the variance sigma, wherein the formula is as follows: r = μ + σ;
step 2.2: an octree structure is used as a data organization mode, each cube comprises S points, and S is a positive integer; each small cube is used as a leaf node of the octree to store the coordinate information and the color of the corresponding point;
and step 3: calculating the node size and depth of the octree model;
when the size of the node file is reduced, the load of the memory is reduced, the depth of the octree is deepened when the load is reduced, when the size of the node file is increased, the load of the memory is increased, and the depth of the octree is shallower when the load is increased;
the calculation formula of the leaf node size Y is as follows:
wherein x, y and z respectively represent the length, width and height of the bounding box B; n is the depth of the octree; setting the value of leaf node size Y according to the limit conditions of different computer memories, thereby calculating the minimum value of N according to the formula, and determining the point cloud range covered by the node on each level of the octree according to the depth N of the octree;
and 4, step 4: constructing an octree model;
according to sampling density D, points in three-dimensional model point cloud n Sampling to generate a root node, dividing the rest nodes into eight regions according to spatial positions, respectively sampling points in the eight regions in a parallel mode, and taking point clouds obtained by sampling as eight child nodes of the root node; recursion is carried out, the recursion is stopped until the number of points in the nodes is smaller than a preset threshold value lambda or the depth of the octree reaches a set depth N, and an octree model is output; setting the threshold lambda as the size range of the leaf node file which is artificially set after balancing the load of the memory and the rendering efficiency in the step 3;
density of sampling per layer node D n The calculation formula of (2) is as follows:
D n ＝2 N-1-n *r
wherein n represents the number of layers of the octree at present;
and 5: compressing an octree model requested by a user at a server side by using a website compression technology, and transmitting compressed data to a browser through a network for rendering and displaying;
and 6: and dynamically loading nodes of the octree model on the webpage according to the level detail technology, and rendering the nodes to finally obtain the three-dimensional scene graph of the scene to be reconstructed.
2. The large three-dimensional scene webpage display method based on model compression and asynchronous loading according to claim 1, characterized in that: the specific steps of the step 6 are as follows:
step 6.1: loading data of a root node in the octree model, setting weights of the nodes according to the size of the area of the bounding box projected onto a screen of a server side, wherein the larger the area is, the larger the weight is, then generating a rendering queue according to the weights of the nodes, and arranging the nodes with the larger weights in front of the rendering queue for rendering preferentially;
step 6.2: updating the rendering queue when the viewpoint changes or zooming-in and zooming-out operations are carried out;
setting two queues, wherein the rendering queue is used for storing nodes which are loaded to the local and wait for rendering, and the cache queue is used for storing nodes which are not loaded to the local in the cache; when a user moves the visual angle of the three-dimensional model, traversing the nodes of the octree layer by layer in a breadth-first mode, and performing occlusion rejection and rendering queue updating according to the visibility of the octree nodes at the current visual angle, namely removing some point clouds stored in the octree nodes from a rendering queue when part of the point clouds at the current visual angle are in an occluded state; when the viewpoint changes or the zooming-in and zooming-out operations are carried out, adding the nodes which are not loaded to the local in the nodes of the current layer into a priority queue of a cache module, and preferentially carrying out asynchronous dynamic loading;
step 6.3: after the asynchronous node data loading is finished, judging whether the cache exceeds a preset threshold value beta, and if so, deleting nodes which are not used for a long time from the cache; the threshold value beta is set according to the cache condition of the computer.
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| WO2021043136A1 (en) * | 2019-09-03 | 2021-03-11 | 上海交通大学 | Point cloud projection transmission method, and reception processing method and system |
| CN111179394A (en) * | 2019-11-25 | 2020-05-19 | 苏州智加科技有限公司 | Point cloud scene rendering method, device and equipment |
| CN110908736A (en) * | 2019-11-26 | 2020-03-24 | 广东趣炫网络股份有限公司 | 3D scene loading method, device, equipment and storage medium |
| CN110992460B (en) * | 2019-11-26 | 2023-05-16 | 深圳市毕美科技有限公司 | Model fluency display method, system, device and storage medium for mobile equipment |
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| CN111415406B (en) * | 2020-03-18 | 2023-05-12 | 东南数字经济发展研究院 | Hierarchical and blocking three-dimensional model data classification compression method |
| CN111415401B (en) * | 2020-03-25 | 2023-05-30 | 上海城建信息科技有限公司 | Large-scale scene rendering method based on WebGL |
| CN111617480A (en) * | 2020-06-04 | 2020-09-04 | 珠海金山网络游戏科技有限公司 | Point cloud rendering method and device |
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| CN112256652B (en) * | 2020-10-19 | 2022-09-16 | 济南大学 | Three-dimensional point cloud attribute compression method, system and terminal |
| CN112215951B (en) * | 2020-10-23 | 2022-04-29 | 华中科技大学 | Out-of-core multi-resolution point cloud representation method and point cloud display method |
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