CN112396682B - Visual progressive model browsing method in three-dimensional scene - Google Patents
Visual progressive model browsing method in three-dimensional scene Download PDFInfo
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Abstract
The invention discloses a visual progressive model browsing method in a three-dimensional scene, which comprises the following steps: creating a globally-wide grid cell; progressively loading grid units and attribute information thereof within a camera view field range in a three-dimensional scene; progressively loading three-dimensional model data corresponding to the loaded grid units; carrying out progressive filtering, detection and rendering on the loaded three-dimensional model data; and displaying the rendered three-dimensional model data according to the change of the field of view of the camera, and progressively unloading the browsed three-dimensional model data. The remarkable effects are as follows: by eliminating a large amount of redundant data, only the required three-dimensional model data is loaded, the drawing burden of the system is reduced, and the loading and rendering efficiency is improved.
Description
Technical Field
The invention relates to the technical field of three-dimensional scene display, in particular to a visual progressive model browsing method in a three-dimensional scene.
Background
With the continuous promotion of three-dimensional city construction, the traditional GIS application mainly based on two-dimensional expression cannot meet the requirements of practical application. Driven by this external practical need, three-dimensional GIS becomes a key means of expressing geospatial information. The three-dimensional GIS not only can truly and vividly depict space information, but also can describe structural relationships among space entities in detail, has the characteristic of high fidelity, is recognized more and more widely, and gradually replaces the traditional two-dimensional expression mode in application. However, with the scale-up of three-dimensional model data and the improvement of modeling accuracy, the rapidly-increased three-dimensional model data brings a serious challenge to the processing capability of graphics hardware.
The current three-dimensional data loading mode is to load data into a video memory at one time, queue for rendering, and render how many times when rendering is performed. However, a large amount of data causes many problems, including data network transmission congestion, excessively large occupied memory, reduced system stability, excessive texture and grid data in the video memory, which causes severe video memory resource consumption, reduced rendering and drawing capabilities, and the like.
In addition, when a three-dimensional scene is browsed, due to the fact that a large number of models are difficult to draw and render in real time, displayed pictures are delayed and blocked, the user experience is seriously influenced due to the fact that the frame rate of the pictures is lower than 24 frames, rapid development and popularization of the three-dimensional GIS are restricted, and the three-dimensional GIS becomes an industry difficult problem which needs to be solved urgently.
Therefore, the invention provides a visual progressive model browsing method in a three-dimensional scene, which can be used for rapidly loading large-scale three-dimensional scene data and rapidly rendering a three-dimensional model according to the change of a viewpoint.
Disclosure of Invention
Aiming at the defects of the prior art, the invention aims to provide a visual progressive model browsing method in a three-dimensional scene, which is based on grid units in a global range and corresponding attribute information thereof, a camera judges whether a grid is in a field of view or not by requesting related grid information in the field of view, eliminates the grid which is not in the field of view, and then loads a corresponding three-dimensional model according to coordinates and index information of the grid, so that the drawing burden of a system is reduced, and the loading and rendering efficiency is improved by eliminating a large amount of redundant data and only loading required three-dimensional model data.
In order to achieve the purpose, the technical scheme adopted by the invention is as follows:
a visual progressive model browsing method in a three-dimensional scene is characterized in that: the method comprises the following steps:
step 1: creating a globally-wide grid cell;
step 2: progressively loading grid units and attribute information thereof within a camera view field range in a three-dimensional scene;
and step 3: progressively loading three-dimensional model data corresponding to the loaded grid units;
and 4, step 4: carrying out progressive filtering, detection and rendering on the loaded three-dimensional model data;
and 5: and displaying the rendered three-dimensional model data according to the change of the field of view of the camera, and progressively unloading the browsed three-dimensional model data.
Further, the method for constructing the grid unit in the step 1 comprises the following steps:
setting the size of the grid, calculating from the longitude and latitude zero point according to the map level to generate the grid unit in the global range, and ensuring that any point in the three-dimensional scene has a unique corresponding grid unit.
Further, step 1 generates a JSON file of the grid cell based on the coordinate information and the index information of the grid cell in the generated grid cell.
Further, the specific step of progressively loading the grid cells and the attribute information thereof within the field of view of the camera in step 2 includes:
step 2.1: converting a world coordinate system where the camera is located into a geographic coordinate system through matrix transformation;
step 2.2: acquiring the height, longitude and latitude, near section of a viewing cone and far section information of the viewing cone of a camera under a geographic coordinate system;
step 2.3: determining a field of view range of a camera in a three-dimensional scene;
step 2.4: comparing the field range of the camera with the coordinate information and index information of the grid unit, loading the grid unit and the attribute information thereof in the field range of the camera,
step 2.5: and carrying out weighted calculation on the loaded grid unit.
Further, the process of performing weighting calculation on the loaded grid cells in step 2.5 is as follows:
making a vertical line between the center point of the camera and the ground to obtain an intersection point of the vertical line and the ground;
carrying out extended search to the four weeks of the intersection points according to the intersection points to obtain corresponding grid units;
calculating the distance between the grid unit and the intersection point, and sequencing according to the distance;
and weighting the corresponding grid cells according to the sequence of the distance, wherein the smaller the distance is, the larger the weight value is.
Further, the weight calculation method comprises:
calculating the grid number of a certain grid unit from a central grid unit according to the numbering rule of the grid units;
and calculating the distance according to the grid width, and taking the reciprocal of the distance as a weight value.
Further, the specific steps of the three-dimensional model data progressive loading in step 3 are as follows:
step 3.1: analyzing JSON files of the grid cells;
step 3.2: reading attribute information of grid cells in the JSON file;
step 3.3: and adopting a multithreading mode to load the three-dimensional model data corresponding to the attribute information of the grid unit in blocks.
Further, the attribute information of the mesh unit includes map level information, encoding information, an ID number, and range information of the mesh unit.
Further, the specific steps of performing progressive filtering, detecting and rendering on the loaded three-dimensional model data in the step 4 are as follows:
step 4.1: loading the loaded three-dimensional model data into a memory according to the organization mode of the grid;
step 4.2: according to the weight data carried by the grid unit, carrying out primary shearing on the three-dimensional model data, and carrying out thinning treatment on the data with lower weight;
step 4.3: moving the three-dimensional model data subjected to the primary shearing processing to a video memory;
step 4.4: performing final shearing on the three-dimensional model data based on a viewing cone of the camera, and removing back data and the three-dimensional model data outside the viewing cone range;
step 4.5: and performing depth detection and three-dimensional pipeline rendering on the finally cut three-dimensional model data.
Further, the specific step of progressively unloading the browsed three-dimensional model data in step 5 is as follows:
step 5.1: establishing a data pool to be deleted;
step 5.2: storing the browsed three-dimensional model data to be deleted into a data pool to be deleted, and establishing a time mark;
step 5.3: and carrying out time marking overtime judgment, if the time marking overtime judgment is not carried out, moving the grid unit and the three-dimensional model data corresponding to the grid unit to a display memory, if the time marking overtime judgment is not carried out, transferring the grid unit and the three-dimensional model data corresponding to the grid unit from the display memory to a memory for being rendered, and removing the grid unit which exceeds the time marking overtime judgment again and the three-dimensional model data corresponding to the grid unit from the memory.
The invention has the following remarkable effects:
1) compared with the traditional three-dimensional scene browsing mode, the vision progressive model browsing method adopted by the invention can support the browsing of three-dimensional data of a larger scene, has a higher frame rate and can provide smoother experience for users.
2) Compared with the traditional data loading mode, the grid data loading mode adopted by the invention can support the loading of mass data, and the data volume and the loading efficiency are obviously improved.
3) Compared with the traditional data rendering mode, the memory and video memory resource scheduling method adopted by the invention can effectively reduce the consumption of hardware resources, reduce the dependence on computer hardware and is more beneficial to the popularization of the three-dimensional technology.
4) Compared with the traditional three-dimensional data rendering life cycle, the three-dimensional model data loading and unloading method provided by the invention can effectively avoid the data from residing in the memory and the display memory for a long time and being unloaded prematurely, thereby ensuring the effective utilization of the memory and the display memory resources, avoiding the increase of the waiting time of a user due to the repeated loading of the data and effectively improving the use perception of the user.
Drawings
FIG. 1 is a flow chart of a method of the present invention;
fig. 2 is a schematic diagram of determining whether a grid cell is located within the field of view of the camera.
Detailed Description
The following provides a more detailed description of the embodiments and the operation of the present invention with reference to the accompanying drawings.
As shown in fig. 1, a visual progressive model browsing method in a three-dimensional scene includes the following steps:
step 1: creating a globally-wide grid cell;
since the present embodiment is based on a grid model browsing manner, a grid cell needs to be created first. The construction method of the grid unit comprises the following steps:
setting the size of the cells, calculating from longitude and latitude zero points according to the map level, generating grid cells of global range (the global range is longitude-180, 180 and latitude-90, 90), and ensuring that any point in the three-dimensional scene has a unique corresponding grid cell.
And setting grid unit codes and ID numbers in the generated grids, and recording longitude and latitude information of the grids. For a standard mesh, the upper left and lower right vertex geographic coordinates of the mesh are computed. And organizing the generated information, generating a JSON file and storing the JSON file.
Step 2: the method comprises the following steps of progressively loading grid units and attribute information thereof within a camera view field range in a three-dimensional scene, and specifically comprises the following steps:
step 2.1: the browsing of the three-dimensional scene is essentially that the field range is changed by moving the position and the angle of the camera in the three-dimensional scene, so that a world coordinate system where the camera is located is converted into a geographic coordinate system through matrix transformation in the process of moving the camera;
step 2.2: acquiring information such as the height, longitude and latitude, near section of a view cone, far section of the view cone and the like of a camera under a geographic coordinate system;
step 2.3: determining the field of view range of a camera in the three-dimensional scene (namely the geographic coordinates of the top left corner vertex and the bottom right corner vertex);
step 2.4: comparing the field range of the camera with the coordinate information and index information of the grid unit, loading the grid just falling into the viewport range and the attribute information thereof, and not loading other grids;
the method for judging whether the grid unit needs to be loaded is shown in fig. 2, and the grid unit is judged by comparing the longitude and latitude coordinates of the upper left and the lower right of the camera with the longitude and latitude coordinates of the upper left and the lower right of the grid, and the grid unit can be considered to be loaded when the grid unit falls in the grid, otherwise, the grid unit is not loaded. The specific algorithm is as follows:
Xgrid mesh∈[XThe upper left of the camera is positioned at the left,Xlower right of camera]，
YGrid mesh∈[YThe upper right of the camera is provided with a camera,Yleft lower part of camera]，
Step 2.5: and carrying out weighted calculation on the loaded grid cells:
making a vertical line between the center point of the camera and the ground to obtain an intersection point of the vertical line and the ground;
carrying out extended search to the four weeks of the intersection points according to the intersection points to obtain corresponding grid units;
calculating the distance between the grid unit and the intersection point, and sequencing according to the distance;
giving weights to corresponding grid units according to the sequence of the distance, giving higher weight values [0-1] to grids closer to the intersection point, and giving lower weight values to grids farther away;
the specific method for calculating the distance is to calculate the number of grids spaced from the central grid according to the numbering rule of the grids (the number of grids is a positive integer N), calculate the distance D ═ nW (N > ═ 1) according to the width W of the grids, and take the reciprocal value and the data type of the double precision type as the weight value. The weight value lambda is 1/D.
Through the data loading steps, compared with the traditional data loading mode, the grid data loading mode adopted by the embodiment can support the loading of mass data, and the data volume supporting and the loading efficiency are obviously improved.
And step 3: the progressive loading of the three-dimensional model data corresponding to the loaded grid unit comprises the following specific steps:
step 3.1: because the grid cells and the data are in one-to-one correspondence through geographic coordinates, when the grid is loaded, the JSON file of the grid cell is firstly analyzed;
step 3.2: reading attribute information such as map level information (LOD), grid unit coding information, grid unit ID number, grid range information (geographic coordinates) and the like in grid units in a JSON file;
step 3.3: and loading the corresponding three-dimensional model data into the system, and loading the three-dimensional model data corresponding to the attribute information of the grid unit in blocks by adopting a multithreading mode in order to ensure that a main process of three-dimensional visual browsing is not influenced.
And 4, step 4: the loaded three-dimensional model data is subjected to progressive filtering, detection and rendering, and the method specifically comprises the following steps:
step 4.1: after the three-dimensional model data is loaded, storing the data in a memory according to the organization mode of the grid;
step 4.2: according to the weight data carried by the grid unit, carrying out primary shearing on the three-dimensional model data, and carrying out thinning treatment on the data with lower weight;
step 4.3: when a three-dimensional scene is browsed, moving the three-dimensional model data subjected to primary shearing processing to a video memory;
step 4.4: performing final shearing on the three-dimensional model data based on a viewing cone of the camera, and removing back data and the three-dimensional model data outside the viewing cone range;
step 4.5: and performing depth detection and three-dimensional pipeline (calling OPENGL related function library) rendering on the finally cut three-dimensional model data to finish the overall rendering of the model.
Compared with the traditional data rendering mode, the data rendering mode has the advantages that the memory and video memory resource scheduling method can effectively reduce hardware resource consumption, reduce dependence on computer hardware and be more beneficial to popularization and promotion of a three-dimensional technology.
And 5: displaying rendered three-dimensional model data according to the change of the field of view of the camera;
meanwhile, the browsed data needs to be unloaded along with the movement of the camera, and the specific method for progressively unloading the browsed three-dimensional model data in the embodiment is as follows:
step 5.1: establishing a data pool to be deleted;
step 5.2: storing the browsed three-dimensional model data to be deleted into a data pool to be deleted, and establishing a time mark;
step 5.3: and carrying out time marking overtime judgment, if the time marking overtime judgment is not carried out, moving the grid unit and the three-dimensional model data corresponding to the grid unit to a display memory, if the time marking overtime judgment is not carried out, transferring the grid unit and the three-dimensional model data corresponding to the grid unit from the display memory to a memory for being rendered, and removing the grid unit which exceeds the time marking overtime judgment again and the three-dimensional model data corresponding to the grid unit from the memory.
Compared with the traditional three-dimensional data rendering life cycle, the three-dimensional model data loading and unloading method in the steps 3 and 5 can effectively avoid the data from residing in the memory and the display memory for a long time and not being unloaded too early, so that the effective utilization of the memory and the display memory resources is ensured, the repeated loading of the data is avoided, the waiting time of a user is prolonged, and the use perception of the user is improved.
The technical solution provided by the present invention is described in detail above. The principles and embodiments of the present invention are explained herein using specific examples, which are presented only to assist in understanding the method and its core concepts. It should be noted that, for those skilled in the art, it is possible to make various improvements and modifications to the present invention without departing from the principle of the present invention, and those improvements and modifications also fall within the scope of the claims of the present invention.
Claims (7)
1. A visual progressive model browsing method in a three-dimensional scene is characterized by comprising the following steps:
step 1: creating a globally-wide grid cell;
step 2: progressively loading grid units and attribute information thereof within a camera view field range in a three-dimensional scene;
the specific step of progressively loading the grid cells and the attribute information thereof within the field of view of the camera in the step 2 comprises:
step 2.1: converting a world coordinate system where the camera is located into a geographic coordinate system through matrix transformation;
step 2.2: acquiring the height, longitude and latitude, near section of a viewing cone and far section information of the viewing cone of a camera under a geographic coordinate system;
step 2.3: determining a field of view range of a camera in a three-dimensional scene;
step 2.4: comparing the field range of the camera with the coordinate information and index information of the grid unit, loading the grid unit and the attribute information thereof in the field range of the camera,
step 2.5: carrying out weighted calculation on the loaded grid unit;
the process of performing weighting calculation on the loaded grid cells in step 2.5 is as follows:
making a vertical line between the center point of the camera and the ground to obtain an intersection point of the vertical line and the ground;
carrying out extended search to the four weeks of the intersection points according to the intersection points to obtain corresponding grid units;
calculating the distance between the grid unit and the intersection point, and sequencing according to the distance;
weighting the corresponding grid units according to the sequence of the distance, wherein the smaller the distance is, the larger the weight value is;
and step 3: progressively loading three-dimensional model data corresponding to the loaded grid units;
and 4, step 4: carrying out progressive filtering, detection and rendering on the loaded three-dimensional model data;
the specific steps of carrying out progressive filtering, detection and rendering on the loaded three-dimensional model data in the step 4 are as follows:
step 4.1: loading the loaded three-dimensional model data into a memory according to the organization mode of the grid;
step 4.2: according to the weight data carried by the grid unit, carrying out primary shearing on the three-dimensional model data, and carrying out thinning treatment on the data with lower weight;
step 4.3: moving the three-dimensional model data subjected to the primary shearing processing to a video memory;
step 4.4: performing final shearing on the three-dimensional model data based on a viewing cone of the camera, and removing back data and the three-dimensional model data outside the viewing cone range;
step 4.5: performing depth detection and three-dimensional pipeline rendering on the finally cut three-dimensional model data;
and 5: and displaying the rendered three-dimensional model data according to the change of the field of view of the camera, and progressively unloading the browsed three-dimensional model data.
2. The visual progressive model browsing method in the three-dimensional scene according to claim 1, wherein the construction method of the grid cells in the step 1 is as follows:
setting the size of the grid, calculating from the longitude and latitude zero point according to the map level to generate the grid unit in the global range, and ensuring that any point in the three-dimensional scene has a unique corresponding grid unit.
3. The method for browsing visually progressive models in a three-dimensional scene according to claim 2, wherein step 1 generates JSON files of grid cells based on coordinate information and index information of the grid cells in the generated grid cells.
4. The method for model browsing in a three-dimensional scene in a visual progression manner according to claim 1, wherein the weight is calculated by:
calculating the grid number of a certain grid unit from a central grid unit according to the numbering rule of the grid units;
and calculating the distance according to the grid width, and taking the reciprocal of the distance as a weight value.
5. The method for browsing a model in a three-dimensional scene in a progressive manner according to claim 1, wherein the specific step of loading three-dimensional model data in a progressive manner in step 3 is as follows:
step 3.1: analyzing JSON files of the grid cells;
step 3.2: reading attribute information of grid cells in the JSON file;
step 3.3: and adopting a multithreading mode to load the three-dimensional model data corresponding to the attribute information of the grid unit in blocks.
6. The method of claim 5, wherein the attribute information of the grid cell comprises map level information, coding information, ID number and range information of the grid cell.
7. The method for browsing a model in a three-dimensional scene in a progressive manner according to claim 1, wherein the step 5 of progressively unloading the browsed three-dimensional model data comprises the following specific steps:
step 5.1: establishing a data pool to be deleted;
step 5.2: storing the browsed three-dimensional model data to be deleted into a data pool to be deleted, and establishing a time mark;
step 5.3: and carrying out time marking overtime judgment, if the time marking overtime judgment is not carried out, moving the grid unit and the three-dimensional model data corresponding to the grid unit to a display memory, if the time marking overtime judgment is not carried out, transferring the grid unit and the three-dimensional model data corresponding to the grid unit from the display memory to a memory for being rendered, and removing the grid unit which exceeds the time marking overtime judgment again and the three-dimensional model data corresponding to the grid unit from the memory.
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