CN115705444B - Method, device and electronic device for generating LOD data - Google Patents
Method, device and electronic device for generating LOD dataInfo
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Abstract
本发明涉及计算机辅助设计技术领域，具体涉及LOD数据的生成方法、装置及电子设备，所述方法包括获取原始模型数据以及初始简化参数；基于所述初始简化参数对所述原始模型数据进行简化得到简化后模型数据，并确定所述原始模型数据与所述简化后模型数据的相似度；根据所述相似度对所述初始简化参数进行调整，以对所述原始模型数据进行再次简化，确定所述原始模型数据对应的LOD数据。由于整个LOD数据的生成过程是自动处理的，不需要人为干预，提高了生成LOD数据的效率。
The present invention relates to the field of computer-aided design technology, and more particularly to a method, apparatus, and electronic device for generating LOD data. The method comprises obtaining original model data and initial simplification parameters; simplifying the original model data based on the initial simplification parameters to obtain simplified model data, and determining a similarity between the original model data and the simplified model data; and adjusting the initial simplification parameters based on the similarity to further simplify the original model data and determine LOD data corresponding to the original model data. Because the entire LOD data generation process is automated and does not require human intervention, the efficiency of LOD data generation is improved.
Description
Technical Field
The invention relates to the technical field of computer aided design, in particular to a LOD data generation method and device and electronic equipment.
Background
At present, 3D display technology is widely applied, plays an important role in the fields of industrial design, games, virtual reality, augmented reality, film and television and the like, and along with the continuous improvement of the requirements on picture fineness and sense of reality, the data volume of 3D display is also greatly increased. However, due to limited hardware performance, if a fine model of an entire scene is loaded at one time, a great amount of hardware resources are required, which causes performance problems such as jamming and crashing, so a level of Detail (LOD) strategy is generated for loading coarse models of distant locations and unimportant contents instead of loading fine models of distant locations and unimportant contents according to different Levels of Detail fine division, and the fine models of distant locations and unimportant contents are loaded when the distance is close or the distance is focused.
Existing LOD data is typically created manually by a modeler or generated by adjusting a number of parameters with a tool. The manual creation needs to repeatedly modify the final molding, the tool is used for generating, the parameters are required to be continuously adjusted to obtain better results, and the quality of the results is judged by technicians in many cases, so that the method has strong subjectivity. Thus, the fineness of existing LOD data depends on a modeler, and LOD effects in a large number of three-dimensional scenes are obtained by manual creation by the modeler, resulting in low efficiency in generating LOD data.
Disclosure of Invention
In view of this, the embodiment of the invention provides a method, a device and an electronic device for generating LOD data, so as to solve the problem of low efficiency of generating LOD data.
According to a first aspect, an embodiment of the present invention provides a method for generating LOD data, including:
Acquiring original model data and initial simplification parameters;
Simplifying the original model data based on the initial simplification parameters to obtain simplified model data, and determining the similarity between the original model data and the simplified model data;
and adjusting the initial simplification parameters according to the similarity so as to simplify the original model data again and determine LOD data corresponding to the original model data.
According to the LOD data generation method provided by the embodiment of the invention, the initial simplification parameters are adjusted through the similarity between the original model data and the simplified model data, the automatic adjustment of the initial simplification parameters is realized, the automatic adjustment is carried out depending on the similarity, the similarity is obtained by utilizing the simplified model data and the original model data, the reliability of the initial simplification parameters after the automatic adjustment can be ensured on the basis of keeping the characteristics of the original model data to the greatest extent, and meanwhile, because the whole LOD data generation process is automatically processed, no human intervention is needed, and the LOD data generation efficiency is improved.
With reference to the first aspect, in a first implementation manner of the first aspect, the adjusting the initial simplification parameter according to the similarity to simplify the original model data again, and determining LOD data corresponding to the original model data includes:
acquiring an initial upper limit and an initial lower limit corresponding to the initial simplified parameters;
Based on the magnitude relation between the similarity and the threshold value, the initial simplification parameters are adjusted by utilizing the initial upper limit and the initial lower limit, and adjusted simplification parameters are obtained;
And simplifying the original model data again by using the adjusted simplifying parameters so as to determine LOD data corresponding to the original model data.
According to the LOD data generation method provided by the embodiment of the invention, the threshold value represents the expected value of simplification, the adjustment direction of the initial simplification parameters is guided through the size relation between the similarity and the threshold value, namely, the initial simplification parameters are adjusted through the initial upper limit and the initial lower limit, the accuracy of the adjusted simplification parameters is ensured, the LOD data which meets the threshold value requirement and is optimal in data quantity is dynamically obtained, the LOD level generation process is automatic, manual intervention is not needed, the automation level is improved, and the loading speed and interactive experience are improved by obtaining the data.
With reference to the first implementation manner of the first aspect, in a second implementation manner of the first aspect, the adjusting the initial simplification parameter by using the initial upper limit and the initial lower limit based on the magnitude relation between the similarity and the threshold value, to obtain an adjusted simplification parameter includes:
adjusting the initial upper limit and the initial lower limit by utilizing the size relation between the similarity and the threshold value to obtain an adjusted upper limit and an adjusted lower limit;
and adjusting the initial simplifying parameters based on the adjusted upper limit and lower limit to obtain the adjusted simplifying parameters.
According to the LOD data generation method provided by the embodiment of the invention, the initial upper limit and the initial lower limit are adjusted by utilizing the magnitude relation between the similarity and the threshold value, and the initial simplification parameters are adjusted on the basis because the threshold value is the expected value for representing simplification, so that the adjustment times of the initial simplification parameters can be reduced, and the LOD data generation efficiency is improved.
With reference to the second embodiment of the first aspect, in a third embodiment of the first aspect, the adjusting the initial upper limit and the initial lower limit by using the magnitude relation between the similarity and the threshold value to obtain an adjusted upper limit and lower limit includes:
When the similarity is smaller than the threshold value, increasing the initial lower limit to obtain an adjusted lower limit;
and when the similarity is larger than the threshold value, reducing the initial upper limit to obtain an adjusted upper limit.
According to the LOD data generation method provided by the embodiment of the invention, when the similarity is larger than the threshold value, the parameters which are closer to the lower limit can be used for simplification, and when the similarity is smaller than the threshold value, the parameters which are closer to the upper limit can be used for simplification, and based on the similarity, the initial upper limit and the lower limit are correspondingly adjusted so as to meet the adjustment requirement.
With reference to the second implementation manner or the third implementation manner of the first aspect, in a fourth implementation manner of the first aspect, the adjusting the initial simplifying parameter based on the adjusted upper limit and the adjusted lower limit to obtain an adjusted simplifying parameter includes:
calculating the average value of the adjusted upper limit and lower limit;
and determining the average value as the adjusted simplified parameter.
According to the LOD data generation method provided by the embodiment of the invention, the adjusted simplified parameters are obtained through a dichotomy, and finally, the global optimal solution meeting the conditions is obtained through iteration.
With reference to the first implementation manner of the first aspect, in a fifth implementation manner of the first aspect, the performing, by using the adjusted simplification parameter, a further simplification on the original model data to determine LOD data corresponding to the original model data includes:
judging whether the difference value between the adjusted upper limit and the adjusted lower limit meets a stop condition or not;
when the stopping condition is not met, the initial simplifying parameters are adjusted by utilizing the adjusted upper limit and lower limit, and the adjusted simplifying parameters are obtained;
And simplifying the original model data again by using the adjusted simplifying parameters so as to determine LOD data corresponding to the original model data.
According to the LOD data generation method provided by the embodiment of the invention, the interval between the upper limit and the lower limit represents the adjustable range of the simplification parameters, and even if model simplification is carried out in a smaller adjustable range, the meaning is not great, so that whether the simplification needs to be continued or not is determined through the relation between the difference value between the adjusted upper limit and lower limit and the stop condition, invalid model simplification is reduced, and the LOD data generation efficiency is improved.
With reference to the first aspect, in a sixth implementation manner of the first aspect, the simplifying the original model data based on the initial simplification parameter to obtain simplified model data, and determining a similarity between the original model data and the simplified model data includes:
simplifying the original model data by using the initial simplification parameters to obtain simplified model data;
converting the original model data and the simplified model data into two-dimensional representations respectively to obtain an original data image and a simplified data image;
The similarity is determined based on features of the original data image and features of the reduced data image.
According to the LOD data generation method provided by the embodiment of the invention, the similarity is calculated by acquiring the two-dimensional image based on the three-dimensional model instead of directly calculating through the three-dimensional model, and the processing speed is high through the two-dimensional image, because the calculation unit based on the similarity of the two-dimensional image is a pixel, the calculation amount of the calculation unit can be tens of thousands to hundreds of thousands. The three-dimensional model is based on three-dimensional model calculation, the calculation unit is the number of triangles or the number of vertexes, which is possibly tens of millions or more, the two pictures are more visual, the effect of the two pictures is easy to be seen, the three-dimensional model is required to be displayed in a three-dimensional space, the material information is easier to process, after the pictures are generated, the geometric information and the material information of the model can be simultaneously embodied, the algorithm for comparing based on the three-dimensional model is mostly focused on the geometric information, the three-dimensional model is more practical, and when the three-dimensional model is displayed on a screen, the three-dimensional model is collapsed into two dimensions, so that when people subjectively judge whether the two models are similar, the two models are judged based on the two-dimensional picture.
According to a second aspect, an embodiment of the present invention further provides a device for generating LOD data, including:
the acquisition module is used for acquiring the original model data and the initial simplification parameters;
The simplifying module is used for simplifying the original model data based on the initial simplifying parameters to obtain simplified model data, and determining the similarity between the original model data and the simplified model data;
And the adjustment module is used for adjusting the initial simplification parameters according to the similarity so as to simplify the original model data again and determine LOD data corresponding to the original model data.
According to the LOD data generating device provided by the embodiment of the invention, the initial simplifying parameters are adjusted through the similarity between the original model data and the simplified model data, so that the automatic adjustment of the initial simplifying parameters is realized, the automatic adjustment is performed depending on the similarity, the similarity is obtained by utilizing the simplified model data and the original model data, the reliability of the initial simplifying parameters after the automatic adjustment can be ensured on the basis of keeping the characteristics of the original model data to the greatest extent, and meanwhile, because the whole LOD data generating process is automatically processed, no human intervention is required, and the LOD data generating efficiency is improved.
According to a third aspect, an embodiment of the present invention provides an electronic device, including a memory and a processor, where the memory and the processor are communicatively connected to each other, and the memory stores computer instructions, and the processor executes the computer instructions, thereby executing the LOD data generating method in the first aspect or any implementation manner of the first aspect.
According to a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium storing computer instructions for causing a computer to perform the method for generating LOD data described in the first aspect or any implementation manner of the first aspect.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are needed in the description of the embodiments or the prior art will be briefly described, and it is obvious that the drawings in the description below are some embodiments of the present invention, and other drawings can be obtained according to the drawings without inventive effort for a person skilled in the art.
FIGS. 1 a-1 d show schematic diagrams of LOD data;
FIG. 1e shows a schematic application of LOD data at different levels;
FIG. 2 is a flow chart of a method of generating LOD data according to an embodiment of the present invention;
FIGS. 3 a-3 b show schematic diagrams of raw model data and simplified model data when viewed at close-up;
FIGS. 3 c-3 d are schematic diagrams showing raw model data and simplified model data when viewed from a distance;
FIG. 4 is a flow chart of a method of generating LOD data according to an embodiment of the present invention;
FIG. 5 is a flow chart of a method of generating LOD data according to an embodiment of the present invention;
FIG. 6 is a flow chart of a method of generating LOD data according to an embodiment of the present invention;
Fig. 7 is a block diagram of a structure of an LOD data generating apparatus according to an embodiment of the present invention;
fig. 8 is a schematic diagram of a hardware structure of an electronic device according to an embodiment of the present invention.
Detailed Description
For the purpose of making the objects, technical solutions and advantages of the embodiments of the present invention more apparent, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention, and it is apparent that the described embodiments are some embodiments of the present invention, but not all embodiments of the present invention. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
LOD is the degree of refinement of a graphical display in which the primitives are represented by different levels. If a geometric figure is far from the viewing position, it can be replaced with a rough morphology without visually significant differences. Specifically, in the graphic display, all planes and curved surfaces are composed of triangular grids or quadrilateral grids, and the finer the geometric shape is, the more triangles are required, and the higher the requirement on the computing performance is. In other words, under a certain computing performance, it is more difficult to display a finer model, and if the model is too fine, a situation such as a jam or breakdown may occur. Therefore, it is necessary to display the LOD data.
Fig. 1 a-1 d show LOD data at different levels, wherein the finesse is progressively reduced. The higher-definition LOD data may be loaded at a position closer to the observation position, and the lower-definition LOD data may be loaded at a position farther from the observation position. That is, FIG. 1e illustrates a specific application of LOD data for each level in FIGS. 1 a-1 d. It should be noted that fig. 1 a-1 e are only for the purpose of representing LOD data at different levels, and are not photographs or filled-in pictures.
Therefore, in order to generate LOD data of different levels, the invention provides a solution that the simplification parameters are adaptively adjusted to meet the requirement of generating LOD data of different levels.
It should be noted that, the generating method of LOD data in the embodiment of the present invention may be applied in fields such as building information models, city information models, or geographic information systems. Specifically, the building information model mainly processes the virtualization implementation of the three-dimensional model of the building space, and uses a digitizing technology to process information of each life stage of the building model, wherein the information comprises space geometric information, professional attributes and state information, such as design, construction, operation and maintenance. The geographic information system is used for collecting, storing, calculating, analyzing and the like the geographic distribution data in the earth surface space through a digitizing technology. The urban information model is a system for urban planning, construction, operation and maintenance, which is constructed by adding a building information model of a single building and combining municipal facilities such as urban roads, bridge tunnels, rail transit, underground pipelines and the like on the basis of an urban geographic information system.
According to an embodiment of the present invention, there is provided an embodiment of a method for generating LOD data, it being noted that the steps shown in the flowcharts of the drawings may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is shown in the flowcharts, in some cases the steps shown or described may be performed in an order different from that herein.
In this embodiment, a method for generating LOD data is provided, which may be used in an electronic device, such as a computer, a mobile phone, a tablet computer, etc., fig. 2 is a flowchart of a method for generating LOD data according to an embodiment of the present invention, and as shown in fig. 2, the flowchart includes the following steps:
s11, acquiring original model data and initial simplification parameters.
The original model data is three-dimensional model data, which is generally extracted from a scene and contains geometric information and material information. The original model data is the basis for model simplification to be carried out subsequently.
The geometric information may be generally expressed as a combination of position, normal, uv and index, where position is a set of vector3f data, each data represents a point in space, normal is a set of vector3f data, each data represents a normal direction of a point in the corresponding space, uv is a set of vector2f data, each data represents a material coordinate of a corresponding vertex in the map, index is a set of vector3i data, and each data (i, j, k) represents a triangle composed of an ith vertex, a jth vertex and a kth vertex.
The material information comprises parameters such as diffuse reflection color, metal roughness and the like and maps, wherein the maps comprise normal maps, diffuse reflection maps, illumination maps, shielding maps, self-luminous maps and the like.
Of course, the data included in the original model data is not limited to the geometric information or the material information, but may also include other information, and may be specifically set correspondingly according to the actual requirement, which is not limited in any way. For example, the information contained in the raw model data herein may be determined from the data required for the subsequent reduced model.
The initial simplifying parameters are not limited to the simplifying parameters used in the first simplifying model, but may also represent the current simplifying parameters, and the purpose of using the initial simplifying parameters is merely to distinguish from the simplifying parameters after subsequent adjustment, and are not particularly limited. The number of parameters included in the initial simplified parameters is determined according to a method of a simplified model used later, and specific data of each parameter may be set according to an empirical value, may be determined according to an experimental result, or the like.
S12, simplifying the original model data based on the initial simplification parameters to obtain simplified model data, and determining the similarity between the original model data and the simplified model data.
The electronic equipment utilizes the initial simplification parameters to simplify the original model data, and the simplified model data is obtained. For example, the original model data is simplified, shelled, etc. using the corresponding initial simplification parameters. The method for simplifying the original model data is not limited to a "simplification" method, but may be a shell extraction method, and the like, and only needs to ensure that the fineness of the original model data can be reduced, and the specific method for simplifying is not limited.
When the original model data is simplified by using a shell extraction method, the method adopted comprises a vertex clustering method based on vertex position distribution, an edge collapse iteration method based on distance between vertexes and a surface degradation method based on secondary errors, and when the original model data is simplified by using a shell extraction method, the method adopted comprises the steps of stretching according to a projection contour stretching method or layered contour local stretching combination transmission and the like.
When the simplified model data is obtained by the method, the simplified model data can also be called LOD layer model data, and the simplified model data becomes more blurred when observed at the same distance, namely the simplified model data is a rough version of the original model data, but the data volume is smaller, and the resource requirement and the pressure on a display engine are smaller. For example, fig. 3a shows the raw model data and fig. 3b shows the corresponding simplified model data. By comparison, the refinement of the simplified model data is lower than that of the original model data.
On the other hand, when the observation distance is long, the original data and the LOD data are equivalent in visual effect, and the original model data may be replaced with the simplified model data. For example, fig. 3c shows the raw model data for a long-range view, and fig. 3d shows the simplified model data for a long-range view. By contrast, when viewed remotely, the original model data is visually comparable to the reduced model data and is indistinguishable, so that, to reduce the resource requirements for the display engine, the reduced model data may be used in place of the original model data when viewed remotely. It should be noted that fig. 3 a-3 d are merely for the purpose of characterizing visual representations of simplified front and back model data, and are not photographs or filled-in photographs.
After the simplified model data is obtained, the electronic device can calculate the similarity between the original model data and the simplified model data. For example, the simplified model data and the original model data may be converted into matrix representations, the similarity may be obtained by calculating the distance between the two matrices, the simplified model data and the original model data may be converted into two-dimensional representations, respectively, the similarity may be obtained by calculating the distance between the two-dimensional images, or the similarity may be obtained by extracting the features of the original model data and the simplified model data, and calculating the distance between the two features.
This step will be described in detail later in detail.
And S13, adjusting the initial simplification parameters according to the similarity so as to simplify the original model data again and determine LOD data corresponding to the original model data.
The similarity is used for representing the similarity degree of the simplified model data and the original model data, and the original model data can be simplified again by adjusting initial simplification parameters as long as the similarity is within a certain threshold range. The purpose of the further simplification is to further reduce the data volume of the simplified model data.
For example, for a simplified method, the smaller the simplification parameters, the less fine the simplified model data it yields. Therefore, when the similarity is within a certain threshold value range, the simplifying parameter may be increased to improve the fineness of the model data after simplification.
Wherein, each time the process of simplifying the original model data is performed, it can be considered that one level of LOD data is obtained. For example, the original model data is regarded as LOD data having the highest fineness, the level thereof is defined as L0 level, LOD data of L1 level is obtained after the first simplification process, LOD data of L2 level is obtained after the second simplification process, and so on until LOD data of Ln level satisfying the demand and having the smallest data amount is determined.
It should be noted that the mode of distinguishing the different levels is not limited to the mode of L0-Ln described above, but may be other modes, and the mode is not limited at all, and may be specifically set according to actual requirements.
This step will be described in detail later in detail.
According to the LOD data generation method, the initial simplification parameters are adjusted through the similarity between the original model data and the simplified model data, automatic adjustment of the initial simplification parameters is achieved, the automatic adjustment is conducted in a manner of relying on the similarity, the similarity is obtained through the simplified model data and the original model data, reliability of the initial simplification parameters after automatic adjustment can be guaranteed on the basis of keeping the characteristics of the original model data to the greatest extent, meanwhile, because the whole LOD data generation process is automatically processed, human intervention is not needed, and LOD data generation efficiency is improved.
In this embodiment, a method for generating LOD data is provided, which may be used in an electronic device, such as a computer, a mobile phone, a tablet computer, etc., fig. 4 is a flowchart of a method for generating LOD data according to an embodiment of the present invention, and as shown in fig. 4, the flowchart includes the following steps:
S21, acquiring original model data and initial simplification parameters.
Please refer to the embodiment S11 shown in fig. 2 in detail, which is not described herein.
S22, simplifying the original model data based on the initial simplification parameters to obtain simplified model data, and determining the similarity between the original model data and the simplified model data.
Please refer to the embodiment S12 shown in fig. 2 in detail, which is not described herein.
S23, adjusting the initial simplification parameters according to the similarity so as to simplify the original model data again and determine LOD data corresponding to the original model data.
Specifically, the step S23 includes:
s231, acquiring an initial upper limit and an initial lower limit corresponding to the initial simplification parameters.
As described above, the number of parameters in the initial simplification parameters is specifically determined according to the method of the simplification process. For example, there are a total of 3 simplification parameters in the initial simplification parameters. For each of the reduced parameters, there is an initial upper limit and an initial lower limit.
For example, the simplified parameter a corresponds to a value range of [ a1, a2], and corresponds to an initial upper limit of a1 and an initial lower limit of a2;
simplifying the parameter B, wherein the corresponding value range is [ B1, B2], the corresponding initial upper limit is B1, and the initial lower limit is B2;
The simplified parameter C corresponds to the value range of [ C1, C2], and corresponds to the initial upper limit of C1 and initially appears as C2.
For each simplified parameter, the electronic device does not have to acquire the corresponding value range, but only acquires the corresponding initial upper limit and lower limit, and can perform corresponding setting according to actual requirements.
S232, based on the magnitude relation between the similarity and the threshold, the initial simplifying parameters are adjusted by utilizing the initial upper limit and the initial lower limit, and the adjusted simplifying parameters are obtained.
The specific value of the threshold value is set correspondingly according to actual requirements. The electronic device compares the similarity with a threshold value and determines the adjustment direction of each simplification parameter in the initial simplification parameters. Specifically, the initial simplifying parameters are adjusted by using the initial upper limit and the initial lower limit, and the adjusted simplifying parameters are further determined.
In some optional implementations of this embodiment, the step S232 may include:
(1) And adjusting the initial upper limit and the initial lower limit by utilizing the relation between the similarity and the threshold value to obtain the adjusted upper limit and lower limit.
The initial upper limit defines a direction limit where simplification is not apparent and the initial lower limit defines a direction limit where simplification is apparent. Wherein when the similarity is larger than the threshold, it means that a simplification can be made using parameters closer to the lower limit, i.e. decreasing the initial upper limit to an adjusted upper limit, e.g. for the simplification parameter a the adjusted upper limit a2= (a1+a2)/2, and when the similarity is smaller than the threshold, it means that a more conservative simplification can be made using parameters closer to the upper limit, i.e. increasing the initial lower limit to an adjusted lower limit, e.g. for the simplification parameter a the adjusted lower limit a1= (a1+a2)/2.
When the similarity is larger than the threshold value, the parameters closer to the lower limit can be used for simplification, and when the similarity is smaller than the threshold value, the parameters closer to the upper limit can be used for simplification, and based on the parameters, the initial upper limit and the lower limit are correspondingly adjusted so as to meet adjustment requirements.
(2) And adjusting the initial simplifying parameters based on the adjusted upper limit and lower limit to obtain the adjusted simplifying parameters.
After determining the adjusted upper and lower limits, the electronic device may calculate the adjusted reduced parameters in the following manner:
2.1 A mean of the adjusted upper and lower limits is calculated.
2.2 The mean value is determined as the adjusted reduced parameter.
The adjusted simplified parameters are obtained through a dichotomy, and finally, the global optimal solution meeting the conditions is obtained through iteration.
The initial upper limit and the initial lower limit are adjusted by utilizing the magnitude relation between the similarity and the threshold value, and the threshold value is an expected value representing simplification, so that the initial simplification parameters are adjusted on the basis, the adjustment times of the initial simplification parameters can be reduced, and the generation efficiency of LOD data is improved.
Or after determining the adjusted upper limit and lower limit, the electronic device may also determine a parameter value from the adjusted value range as the adjusted value of the simplified parameter.
Further alternatively, the simplified adjustment parameters may also be obtained as follows. For the simplified parameter A, the initial upper limit is a1, the initial lower limit is a2, and an enumeration method can be adopted to determine the adjusted simplified parameter. That is, the adjusted reduced parameter a=a2+t (a 1-a 2), t=s, 2s,3s,..ns, ns=1, the minimum value of t is chosen for how the condition is thresholded.
For example, the reduced parameter has only one parameter, i.e., the ratio of the number of reduced triangles to the number of reduced front triangles, denoted ratio. Para= (ratio). It is assumed that the ratio minimum may be 0 and the maximum may be 0.5. Then there is a parameter interval [ (0.0), (0.5) ]. Now, it is desirable to find a ratio value such that the similarity is thresholded and the ratio is minimal, assuming that the satisfactory ratio is in the interval. Taking s=0.1, n=10. Then t= 0.1,0.2,0.3,..0.9, 1.0. Para= (0.05), (0.1), (0.45), (0.5). Using para to sequentially calculate whether the threshold condition is met, and assuming that when para= (0.35) does not meet the threshold condition, para= (0.40) meets the threshold condition, then para= (0.40), that is, ratio=0.40, is taken.
Or other modes can be used for adjusting the initial simplification parameters, and corresponding setting can be carried out according to actual requirements.
S233, the original model data is simplified again by utilizing the adjusted simplified parameters so as to determine LOD data corresponding to the original model data.
After obtaining the adjusted simplifying parameters, the electronic device uses the simplifying parameters to simplify the original model data again, and specifically, the simplifying mode described in S22 can be adopted to finally determine LOD data corresponding to the original model data.
According to the LOD data generation method, the threshold value represents the expected value of simplification, the adjustment direction of the initial simplification parameters is guided through the size relation between the similarity and the threshold value, namely, the initial simplification parameters are adjusted through the initial upper limit and the initial lower limit, the accuracy of the adjusted simplification parameters is guaranteed, LOD data which meets the threshold value requirement and is optimal in data quantity is dynamically obtained, the LOD level generation process is automatic, manual intervention is not needed, the automation level is improved, and the loading speed and interactive experience are improved by obtaining the data.
As an optional implementation manner of this embodiment, after the electronic device adjusts the initial upper limit and the initial lower limit, it may first determine whether the size of the parameter interval is within a specific range value, thereby determining whether the process of simplifying the processing is ended. Specifically, the S233 may include:
(1) And judging whether the difference between the adjusted upper limit and the adjusted lower limit meets the stop condition.
For example, the stop condition is D, and for the simplified parameter a, the difference between the adjusted upper limit and the adjusted lower limit is calculated, the value range is determined, and whether the value range is smaller than the stop condition is determined. When the stopping condition is met, the process is ended, and the current simplified result is selected as LOD data which meets the condition and has the minimum data quantity. Otherwise, executing the step (2).
(2) When the stopping condition is not met, the initial simplifying parameters are adjusted by the adjusted upper limit and lower limit, and the adjusted simplifying parameters are obtained.
That is, the adjusted reduced parameters are obtained in the manner described in step (2) in the alternative embodiment of S232 above.
(3) And simplifying the original model data again by using the adjusted simplifying parameters so as to determine LOD data corresponding to the original model data.
Please refer to the simplified method of S22 in detail, which is not described herein.
Because the interval between the upper limit and the lower limit represents the adjustable range of the simplifying parameter, even if the model is simplified, the adjustable range is not significant, so that whether the simplification needs to be continued or not is determined through the relation between the difference value between the adjusted upper limit and the lower limit and the stopping condition, invalid model simplification is reduced, and the generating efficiency of LOD data is improved.
In this embodiment, a method for generating LOD data is provided, which may be used in an electronic device, such as a computer, a mobile phone, a tablet computer, etc., and fig. 5 is a flowchart of a method for generating LOD data according to an embodiment of the present invention, as shown in fig. 5, where the flowchart includes the following steps:
S31, acquiring original model data and initial simplification parameters.
Please refer to the embodiment S11 shown in fig. 2 in detail, which is not described herein.
S32, simplifying the original model data based on the initial simplification parameters to obtain simplified model data, and determining the similarity between the original model data and the simplified model data.
Specifically, the step S32 includes:
s321, simplifying the original model data by using the initial simplification parameters to obtain simplified model data.
Please refer to the simplified method of S22 in detail, which is not described herein.
S322, converting the original model data and the simplified model data into two-dimensional representations respectively to obtain an original data image and a simplified data image.
Because the original model data and the simplified model data are three-dimensional model data, the electronic equipment converts the original model data and the simplified model data into two-dimensional representations respectively, so that the data processing amount is reduced, and an original data image and a simplified data image are obtained. The two-dimensional representation can be generated by selecting left, front, right, back, up, down or oblique directions, other specified directions or multiple directions.
The specific manner of representing the three-dimensional model in a two-dimensional manner is not limited in any way, and the three-dimensional model can be selected according to actual requirements.
S323, determining the similarity based on the features of the original data image and the features of the simplified data image.
After the electronic device obtains the original data image and the simplified data image of the two-dimensional representation, respectively extracting the characteristics of the original data image and the simplified data image, and calculating the similarity by using the extracted characteristics. The feature extraction can be realized by adopting a feature extraction network, or can be realized by adopting an image processing mode to extract the contour lines or pixel colors of the original data image, and at least one of the two modes is utilized to calculate the similarity.
For example, a contour line-based similarity cs may be calculated, and a pixel color-based similarity ps may be subsequently compared with a corresponding threshold to determine an adjusted reduction parameter.
And S33, adjusting the initial simplification parameters according to the similarity so as to simplify the original model data again and determine LOD data corresponding to the original model data.
Please refer to the embodiment S23 shown in fig. 4 in detail, which is not described herein.
According to the LOD data generation method provided by the embodiment, the similarity is calculated by acquiring the two-dimensional image based on the three-dimensional model, rather than directly calculating the LOD data through the three-dimensional model, and the processing speed is high through the two-dimensional image, because the calculation unit based on the similarity of the two-dimensional image is a pixel, the calculation amount of the LOD data can be tens of thousands to hundreds of thousands. The three-dimensional model is based on three-dimensional model calculation, the calculation unit is the number of triangles or the number of vertexes, which is possibly tens of millions or more, the two pictures are more visual, the effect of the two pictures is easy to be seen, the three-dimensional model is required to be displayed in a three-dimensional space, the material information is easier to process, after the pictures are generated, the geometric information and the material information of the model can be simultaneously embodied, the algorithm for comparing based on the three-dimensional model is mostly focused on the geometric information, the three-dimensional model is more practical, and when the three-dimensional model is displayed on a screen, the three-dimensional model is collapsed into two dimensions, so that when people subjectively judge whether the two models are similar, the two models are judged based on the two-dimensional picture.
In a specific application example of the present embodiment, as shown in fig. 6, the method for generating LOD data includes:
(1) Raw model data is acquired.
(2) And simplifying the original model data by using the initial simplification parameters to obtain LOD data.
(3) And respectively carrying out two-dimensional representation on the original model data and the LOD data, namely respectively generating corresponding thumbnails to obtain an original data image and a simplified data image.
Wherein camera distance needs to be considered when generating the corresponding thumbnail.
(4) And performing similarity calculation on the original data image and the simplified data image to obtain a data comparison result.
Specifically, under specific camera parameters and display parameters, the similarity cs based on the contour lines, and the similarity ps based on the pixel colors are calculated. Wherein, the similarity is based on the original data of 1.0, the closer to 1.0 is the more similar, and the closer to 0 is the less similar.
(5) And adjusting the simplifying parameters based on the data comparison result.
And comparing the obtained data comparison result with a preset threshold value, and determining new simplified parameters, wherein the data comparison result is performed in a binary mode. Assuming that the parameters used by the reduced algorithm are para 1,para2,para3,…,paran, the parameter vector para= (para 1,para2,para3,…,paran) can be obtained. For example, the reduced parameters are used and only one is used, i.e., the ratio of the number of reduced triangles to the number of reduced front triangles, denoted as ratio, corresponding to the parameter vector para= (ratio). when the ratio value is smaller, the simplifying effect is more obvious, the data volume is greatly reduced, and when the ratio value is larger, the simplifying effect is less obvious, and the data is close to the original data. Assuming that there is a relatively small ratio value ratio s and a relatively large ratio value ratio l, corresponding to para s＝(ratios),paral＝(ratiol, there is a parameter interval [ para s,paral ]. Wherein the lower boundary para s of the interval defines a simplification apparent directional limit and the upper boundary para l of the interval defines a simplification unobvious directional limit. When the simplification result satisfies the threshold condition, namely cs > cs th,ps>psth, it means that parameters closer to para s can be used for simplification, and para l＝(paras+paral)/2 is updated, whereas when the simplification result does not satisfy the threshold condition, namely cs < cs th or ps < ps th, it means that parameters closer to para l can be used for more conservative simplification, and para s＝(paras+paral)/2 is updated. The judgment conditions of the threshold values can be modified according to requirements.
For example, if the reduced parameter has only one parameter ratio, then para= (ratio). It is assumed that the ratio minimum may be 0.01 and the maximum may be 1.0. Then there is a parameter interval [ (0.01), (1.0) ]. Now, it is desirable to find a ratio value such that the similarity is thresholded and the ratio is minimal, assuming that the satisfactory ratio is in the interval. The para= ((0.01) + (1.0))/2= (0.505) was taken first for calculation. If the threshold condition is met, the ratio must be within the interval [ (0.01), (0.505) ]. Then again, para= ((0.01) + (0.505))/2= 0.2575, and if the threshold condition is not met, the ratio must be within the interval [ (0.2575), (0.505) ]. The ratio can be obtained by repeating the steps a plurality of times until the upper and lower boundaries of the interval are very close.
(6) Judging whether the simplified parameters are converged to a certain degree, if so, stopping the flow, otherwise, returning to the step (2) to continue to simplify the original model data by using the adjusted simplified parameters.
When the size of the parameter interval is converged within a specific value, i.e., |para l-paras|<|parath |, ending the process, selecting the current simplified result as a final result, otherwise, using (para s+paral)/2 as a simplified parameter, and re-executing the step (2).
The embodiment also provides a device for generating LOD data, which is used for implementing the foregoing embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
The present embodiment provides a generating device of LOD data, as shown in fig. 7, including:
an acquisition module 41, configured to acquire original model data and initial simplified parameters;
a simplifying module 42, configured to simplify the original model data based on the initial simplifying parameter to obtain simplified model data, and determine a similarity between the original model data and the simplified model data;
and the adjustment module 43 is configured to adjust the initial simplification parameters according to the similarity, so as to simplify the original model data again, and determine LOD data corresponding to the original model data.
According to the LOD data generation device, the initial simplification parameters are adjusted through the similarity between the original model data and the simplified model data, automatic adjustment of the initial simplification parameters is achieved, the automatic adjustment is conducted in a manner of relying on the similarity, the similarity is obtained through the simplified model data and the original model data, reliability of the initial simplification parameters after automatic adjustment can be guaranteed on the basis of keeping the characteristics of the original model data to the greatest extent, meanwhile, because the whole LOD data generation process is automatically processed, human intervention is not needed, and LOD data generation efficiency is improved.
The LOD data generating means in this embodiment are presented in the form of functional units, where the units refer to ASIC circuits, processors and memories executing one or more software or fixed programs, and/or other devices that can provide the above described functions.
Further functional descriptions of the above respective modules are the same as those of the above corresponding embodiments, and are not repeated here.
The embodiment of the invention also provides electronic equipment, which is provided with the LOD data generating device shown in the figure 7.
Referring to fig. 8, fig. 8 is a schematic structural diagram of an electronic device according to an alternative embodiment of the present invention, and as shown in fig. 8, the electronic device may include at least one processor 51, such as a CPU (Central Processing Unit ), at least one communication interface 53, a memory 54, and at least one communication bus 52. Wherein the communication bus 52 is used to enable connected communication between these components. The communication interface 53 may include a Display screen (Display) and a Keyboard (Keyboard), and the selectable communication interface 53 may further include a standard wired interface and a wireless interface. The memory 54 may be a high-speed RAM memory (Random Access Memory, volatile random access memory) or a non-volatile memory (non-volatile memory), such as at least one disk memory. The memory 54 may alternatively be at least one memory device located remotely from the aforementioned processor 51. Wherein the processor 51 may be as described in connection with fig. 7, the memory 54 stores an application program, and the processor 51 invokes the program code stored in the memory 54 for performing any of the method steps described above.
The communication bus 52 may be a peripheral component interconnect standard (PERIPHERAL COMPONENT INTERCONNECT, PCI) bus, an extended industry standard architecture (extended industry standard architecture, EISA) bus, or the like. The communication bus 52 may be classified as an address bus, a data bus, a control bus, or the like. For ease of illustration, only one thick line is shown in fig. 8, but not only one bus or one type of bus.
The memory 54 may include a volatile memory (english) such as a random-access memory (RAM), a nonvolatile memory (english) such as a flash memory (english), a hard disk (english: HARD DISK DRIVE, HDD) or a solid-state disk (english: solid-STATE DRIVE, SSD), and the memory 54 may include a combination of the above types of memories.
The processor 51 may be a central processor (English: central processing unit, abbreviated: CPU), a network processor (English: network processor, abbreviated: NP) or a combination of CPU and NP.
The processor 51 may further include a hardware chip, among others. The hardware chip may be an application-specific integrated circuit (ASIC), a Programmable Logic Device (PLD), or a combination thereof (English: programmable logic device). The PLD may be a complex programmable logic device (English: complex programmable logic device, abbreviated: CPLD), a field-programmable gate array (English: field-programmable GATE ARRAY, abbreviated: FPGA), a general-purpose array logic (English: GENERIC ARRAY logic, abbreviated: GAL), or any combination thereof.
Optionally, the memory 54 is also used for storing program instructions. The processor 51 may invoke program instructions to implement the method of generating LOD data as shown in any of the embodiments of the present application in fig. 2 or fig. 4-6.
The embodiment of the invention also provides a non-transitory computer storage medium, which stores computer executable instructions, and the computer executable instructions can execute the LOD data generating method in any of the method embodiments. The storage medium may be a magnetic disk, an optical disc, a Read-Only Memory (ROM), a random access Memory (Random Access Memory, RAM), a Flash Memory (Flash Memory), a hard disk (HARD DISK DRIVE, abbreviated as HDD), a Solid state disk (Solid-state disk STATE DRIVE, SSD), or the like, and may further include a combination of the above types of memories.
Although embodiments of the present invention have been described in connection with the accompanying drawings, various modifications and variations may be made by those skilled in the art without departing from the spirit and scope of the invention, and such modifications and variations fall within the scope of the invention as defined by the appended claims.
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