CN112652070B - Three-dimensional model surface reduction method, device, equipment and medium - Google Patents
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Abstract
本发明涉及三维模型技术领域,本发明公开了一种三维模型的减面方法、装置、设备及介质,所述方法包括:通过获取待处理三维文件,构建与待处理三维文件对应的三维模型数据;对三维模型数据进行减面识别,得到减面结果以及与减面结果对应的基准减面值;在减面结果为需减面时,根据基准减面值,确定与三维模型数据中的各子模型对应的目标面数值;根据所有与子模型对应的目标面数值,对三维模型数据做相应的减面;将减面后的三维模型数据压缩成减面三维文件并输出。本发明实现了自动识别减面结果和基准减面值,自动输出目标面数值,并按目标面数值进行相应减面,以及压缩输出,无需人工操作,并提高了三维模型减面的效率,提高了三维模型减面的质量。
The present invention relates to the technical field of three-dimensional models. The present invention discloses a three-dimensional model surface reduction method, device, equipment, and medium. The method includes: constructing three-dimensional model data corresponding to the three-dimensional file to be processed by acquiring the three-dimensional file to be processed ;Recognize the surface reduction of the 3D model data, and obtain the surface reduction result and the corresponding base denomination value of the surface reduction result; when the surface reduction result needs to be reduced, determine the sub-models in the 3D model data according to the base deduction face value The corresponding target surface value; according to all the target surface values corresponding to the sub-models, the corresponding surface reduction is performed on the 3D model data; the 3D model data after surface reduction is compressed into a 3D file for surface reduction and output. The present invention realizes automatic recognition of surface reduction results and reference denomination values, automatically outputs the target surface value, performs corresponding surface reduction according to the target surface value, and compresses the output without manual operation, and improves the efficiency of three-dimensional model surface reduction, improving the The quality of the reduced surface of the 3D model.
Description
技术领域technical field
本发明涉及三维模型技术领域,尤其涉及一种三维模型的减面方法、装置、设备及介质。The invention relates to the technical field of three-dimensional models, in particular to a surface reduction method, device, equipment and medium for a three-dimensional model.
背景技术Background technique
三维模型已经广泛应用于各个领域,例如家居行业、建筑行业、医疗行业、电影行业、视频游戏产业以及在科学研究及工程应用等领域。三维模型物体是由三角形面片组成,三维模型的渲染及制作对硬件系统和建模软件的要求极高。一般三维模型的面数从几十到数百万不等,硬件配置过低的电脑处理稍微复杂的三维模型则会出现不同程度的卡顿,模型渲染的时间也随模型的复杂程度及模型的面数变化,因此三维模型的三角面数是影响模型渲染时效率和帧数的关键因素。3D models have been widely used in various fields, such as household industry, construction industry, medical industry, film industry, video game industry, as well as in scientific research and engineering applications. 3D model objects are composed of triangular faces, and the rendering and production of 3D models have extremely high requirements on hardware systems and modeling software. Generally, the number of faces of a 3D model ranges from dozens to millions. Computers with low hardware configuration will experience different degrees of lag when processing slightly complex 3D models. The time for model rendering also varies with the complexity of the model and the size of the model. The number of faces changes, so the number of triangular faces of the 3D model is a key factor affecting the efficiency and frame rate of the model rendering.
目前,现有技术主要采用以下两种方法对三维模型进行减面:第一种是使用一些主流建模软件官方提供的相关减面插件进行一定程度的自动减面;第二种是按照原模型的轮廓,重新构造出一个简化的模型,或者在原模型的基础上通过人工进行手动减面。At present, the existing technology mainly adopts the following two methods to reduce the surface of the 3D model: the first is to use the related surface reduction plug-ins officially provided by some mainstream modeling software to automatically reduce the surface to a certain extent; the second is to use the original model Contour, reconstruct a simplified model, or manually reduce the surface on the basis of the original model.
然而,虽然上述两种方法均可实现对三维模型进行减面,但是却有不同程度的缺陷存在。具体的,方法一中所使用的插件一般都需要付费,而且多边形减面工具生成的结果无法满足特殊需求,很难针对模型的特点进行精细处理,三角面片减到一定的比例就会出现不同程度的走样甚至变形;方法二需要以人眼来判断减面的效果,该方法虽然效果好,面片数量可人为控制,但是费时费工,人力成本极高,无法适应大规模化的模型处理。However, although the above two methods can reduce the surface of the 3D model, they have different degrees of defects. Specifically, the plug-ins used in method 1 generally need to be paid, and the results generated by the polygon reduction tool cannot meet special needs, and it is difficult to fine-tune the characteristics of the model, and the triangle surface will be different when it is reduced to a certain proportion. The degree of aliasing or even deformation; the second method needs to judge the effect of surface reduction with human eyes. Although this method is effective and the number of patches can be controlled artificially, it is time-consuming and labor-intensive, and the labor cost is extremely high, which cannot adapt to large-scale model processing. .
故,有必要提供一种技术方案,以解决上述技术问题。Therefore, it is necessary to provide a technical solution to solve the above technical problems.
发明内容Contents of the invention
本发明提供一种三维模型的减面方法、装置、计算机设备及存储介质,实现了自动完成三维模型的减面操作,无需人工操作,减少了人工成本,并提高了三维模型减面的效率,以及保证了三维模型减面的有效性和不失真,提高了三维模型减面的质量。The invention provides a three-dimensional model surface reduction method, device, computer equipment and storage medium, which realizes the automatic completion of the three-dimensional model surface reduction operation without manual operation, reduces labor costs, and improves the efficiency of three-dimensional model surface reduction. In addition, the effectiveness and no distortion of the surface reduction of the 3D model are guaranteed, and the quality of the surface reduction of the 3D model is improved.
一种三维模型的减面方法,包括:A method for surface reduction of a three-dimensional model, comprising:
获取待处理三维文件,构建与所述待处理三维文件对应的三维模型数据;Acquiring the three-dimensional file to be processed, and constructing three-dimensional model data corresponding to the three-dimensional file to be processed;
对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值;Perform surface reduction recognition on the three-dimensional model data to obtain a surface reduction result and a reference surface reduction value corresponding to the surface reduction result;
在所述减面结果为需减面时,根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值;When the surface reduction result is surface reduction, according to the reference denomination reduction value, determine the target denomination value corresponding to each sub-model in the three-dimensional model data;
根据所有与所述子模型对应的目标面数值,对所述三维模型数据做相应的减面;Perform corresponding surface reduction on the three-dimensional model data according to all target surface values corresponding to the sub-models;
将减面后的所述三维模型数据压缩成减面三维文件并输出;Compressing the three-dimensional model data after surface reduction into a surface-reduction three-dimensional file and outputting it;
所述对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值,包括:The performing surface reduction identification on the 3D model data to obtain a surface reduction result and a reference surface reduction value corresponding to the surface reduction result, including:
对所述三维模型数据进行平面数据收集,得到多个平面数据;所述平面数据包括平面图像、平面面数和平面尺寸;Carrying out plane data collection on the three-dimensional model data to obtain a plurality of plane data; the plane data includes plane images, plane numbers and plane dimensions;
将所有所述平面图像输入减面识别模型,通过所述减面识别模型对所有所述平面图像进行纹理特征提取,获取所述减面识别模型根据提取的所述纹理特征识别出的所述减面结果;Inputting all the planar images into the surface reduction recognition model, performing texture feature extraction on all the planar images through the surface reduction recognition model, and obtaining the reduced surface identified by the surface reduction recognition model according to the extracted texture features. Surface results;
在所述减面结果为需减面时,将各所述平面数据中的所述平面面数和所述平面尺寸进行向量转换,得到与所述三维模型数据对应的向量矩阵;When the surface reduction result is a surface to be reduced, vector conversion is performed on the number of plane surfaces and the plane size in each of the plane data to obtain a vector matrix corresponding to the three-dimensional model data;
将所述向量矩阵输入基准识别模型,通过所述基准识别模型对所述向量矩阵进行基准减面预测,得到与所述减面结果对应的所述基准减面值。The vector matrix is input into a reference recognition model, and the reference face reduction prediction is performed on the vector matrix through the reference recognition model to obtain the reference face reduction value corresponding to the result of the face reduction.
一种三维模型的减面装置,包括:A surface reduction device for a three-dimensional model, comprising:
获取模块,用于获取待处理三维文件,构建与所述待处理三维文件对应的三维模型数据;An acquisition module, configured to acquire a three-dimensional file to be processed, and construct three-dimensional model data corresponding to the three-dimensional file to be processed;
识别模块,用于对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值;An identification module, configured to perform area reduction identification on the three-dimensional model data, to obtain an area reduction result and a reference area reduction value corresponding to the area reduction result;
确定模块,用于在所述减面结果为需减面时,根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值;A determination module, configured to determine the target denomination corresponding to each sub-model in the 3D model data according to the reference denomination denomination when the denomination result is the denomination required;
减面模块,用于根据所有与所述子模型对应的目标面数值,对所述三维模型数据做相应的减面;A surface reduction module, configured to perform corresponding surface reduction on the 3D model data according to all target surface values corresponding to the sub-models;
输出模块,用于将减面后的所述三维模型数据压缩成减面三维文件并输出;An output module, configured to compress the three-dimensional model data after surface reduction into a surface-reduction three-dimensional file and output it;
所述识别模块还用于:The identification module is also used for:
对所述三维模型数据进行平面数据收集,得到多个平面数据;所述平面数据包括平面图像、平面面数和平面尺寸;Carrying out plane data collection on the three-dimensional model data to obtain a plurality of plane data; the plane data includes plane images, plane numbers and plane dimensions;
将所有所述平面图像输入减面识别模型,通过所述减面识别模型对所有所述平面图像进行纹理特征提取,获取所述减面识别模型根据提取的所述纹理特征识别出的所述减面结果;Inputting all the planar images into the surface reduction recognition model, performing texture feature extraction on all the planar images through the surface reduction recognition model, and obtaining the reduced surface identified by the surface reduction recognition model according to the extracted texture features. Surface results;
在所述减面结果为需减面时,将各所述平面数据中的所述平面面数和所述平面尺寸进行向量转换,得到与所述三维模型数据对应的向量矩阵;When the surface reduction result is a surface to be reduced, vector conversion is performed on the number of plane surfaces and the plane size in each of the plane data to obtain a vector matrix corresponding to the three-dimensional model data;
将所述向量矩阵输入基准识别模型,通过所述基准识别模型对所述向量矩阵进行基准减面预测,得到与所述减面结果对应的所述基准减面值。The vector matrix is input into a reference recognition model, and the reference face reduction prediction is performed on the vector matrix through the reference recognition model to obtain the reference face reduction value corresponding to the result of the face reduction.
一种计算机设备,包括存储器、处理器以及存储在所述存储器中并可在所述处理器上运行的计算机程序,所述处理器执行所述计算机程序时实现上述三维模型的减面方法的步骤。A computer device, comprising a memory, a processor, and a computer program stored in the memory and operable on the processor, when the processor executes the computer program, the steps of the above-mentioned three-dimensional model surface reduction method are realized .
一种计算机可读存储介质,所述计算机可读存储介质存储有计算机程序,所述计算机程序被处理器执行时实现上述三维模型的减面方法的步骤。A computer-readable storage medium, where a computer program is stored in the computer-readable storage medium, and when the computer program is executed by a processor, the steps of the above-mentioned three-dimensional model surface reduction method are realized.
本发明提供的三维模型的减面方法、装置、计算机设备及存储介质,通过获取待处理三维文件,构建与所述待处理三维文件对应的三维模型数据;对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值;在所述减面结果为需减面时,根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值;根据所有与所述子模型对应的目标面数值,对所述三维模型数据做相应的减面;将减面后的所述三维模型数据压缩成减面三维文件并输出,如此,实现了通过构建三维模型数据,自动识别减面结果和基准减面值,在减面结果为需减面时,自动输出三维模型数据中的各子模型的目标面数值,并对各子模型减面按与其对应的目标面数值进行相应减面,并压缩输出减面三维文件,达到自动完成三维模型的减面操作,无需人工操作,减少了人工成本,并提高了三维模型减面的效率,以及保证了三维模型减面的有效性和不失真,提高了三维模型减面的质量。The three-dimensional model surface reduction method, device, computer equipment, and storage medium provided by the present invention construct three-dimensional model data corresponding to the three-dimensional file to be processed by acquiring the three-dimensional file to be processed; perform surface reduction recognition on the three-dimensional model data , to obtain the surface reduction result and the reference surface reduction value corresponding to the surface reduction result; when the surface reduction result is that surface reduction is required, according to the reference surface reduction value, it is determined to correspond to each sub-model in the three-dimensional model data The value of the target surface; according to all the target surface values corresponding to the sub-models, the corresponding surface reduction is performed on the three-dimensional model data; the three-dimensional model data after the surface reduction is compressed into a surface-reduction three-dimensional file and output, so , to realize the automatic recognition of the surface reduction result and the reference surface value by constructing the 3D model data, and automatically output the target surface value of each sub-model in the 3D model data when the surface reduction result needs to be reduced, and reduce the surface value of each sub-model The surface is reduced according to the corresponding target surface value, and the 3D file of surface reduction is compressed and output, so as to automatically complete the surface reduction operation of the 3D model, without manual operation, which reduces labor costs and improves the efficiency of surface reduction of the 3D model. In addition, the effectiveness and no distortion of the surface reduction of the 3D model are guaranteed, and the quality of the surface reduction of the 3D model is improved.
附图说明Description of drawings
为了更清楚地说明本发明实施例的技术方案,下面将对本发明实施例的描述中所需要使用的附图作简单地介绍,显而易见地,下面描述中的附图仅仅是本发明的一些实施例,对于本领域普通技术人员来讲,在不付出创造性劳动性的前提下,还可以根据这些附图获得其他的附图。In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following will briefly introduce the accompanying drawings that need to be used in the description of the embodiments of the present invention. Obviously, the accompanying drawings in the following description are only some embodiments of the present invention , for those skilled in the art, other drawings can also be obtained according to these drawings without paying creative labor.
图1是本发明一实施例中三维模型的减面方法的应用环境示意图;Fig. 1 is a schematic diagram of the application environment of the surface reduction method of the three-dimensional model in an embodiment of the present invention;
图2是本发明一实施例中三维模型的减面方法的流程图;Fig. 2 is a flowchart of a method for reducing the surface of a three-dimensional model in an embodiment of the present invention;
图3是本发明一实施例中三维模型的减面方法的步骤S20的流程图;Fig. 3 is a flow chart of step S20 of the surface reduction method of a three-dimensional model in an embodiment of the present invention;
图4是本发明另一实施例中三维模型的减面方法的步骤S20的流程图;Fig. 4 is a flow chart of step S20 of the surface reduction method of a three-dimensional model in another embodiment of the present invention;
图5是本发明一实施例中三维模型的减面方法的步骤S30的流程图;Fig. 5 is a flow chart of step S30 of the surface reduction method of a three-dimensional model in an embodiment of the present invention;
图6是本发明一实施例中三维模型的减面装置的转换模块的原理框图;Fig. 6 is a functional block diagram of a conversion module of a surface reduction device for a three-dimensional model in an embodiment of the present invention;
图7是本发明一实施例中计算机设备的示意图。Fig. 7 is a schematic diagram of a computer device in an embodiment of the present invention.
具体实施方式detailed description
下面将结合本发明实施例中的附图,对本发明实施例中的技术方案进行清楚、完整地描述,显然,所描述的实施例是本发明一部分实施例,而不是全部的实施例。基于本发明中的实施例,本领域普通技术人员在没有作出创造性劳动前提下所获得的所有其他实施例,都属于本发明保护的范围。The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without creative efforts fall within the protection scope of the present invention.
本发明提供的三维模型的减面方法,可应用在如图1的应用环境中,其中,客户端(计算机设备)通过网络与服务器进行通信。其中,客户端(计算机设备)包括但不限于为各种个人计算机、笔记本电脑、智能手机、平板电脑和便携式可穿戴设备。服务器可以用独立的服务器或者是多个服务器组成的服务器集群来实现。The surface reduction method of the 3D model provided by the present invention can be applied in the application environment as shown in Fig. 1, wherein the client (computer device) communicates with the server through the network. Wherein, the client (computer device) includes but is not limited to various personal computers, notebook computers, smart phones, tablet computers and portable wearable devices. The server can be implemented by an independent server or a server cluster composed of multiple servers.
在一实施例中,如图2所示,提供一种三维模型的减面方法,其技术方案主要包括以下步骤S10-S50:In one embodiment, as shown in FIG. 2 , a method for surface reduction of a three-dimensional model is provided, and its technical solution mainly includes the following steps S10-S50:
S10,获取待处理三维文件,构建与所述待处理三维文件对应的三维模型数据。S10. Acquire a 3D file to be processed, and construct 3D model data corresponding to the 3D file to be processed.
可理解地,所述待处理三维文件为未进行加工且需要进行减面处理的原始的三维模型的文件,所述构建所述三维模型数据的过程为通过应用程序软件打开所述待处理三维文件,比如应用程序软件为3dsMax软件,通过3dsMax软件可以构建出所述三维模型数据,所述三维模型数据为通过所述应用程序软件解码展示的数据,所述三维模型数据包括三维模型和该三维模型下的各个子模型,所述三维模型为具有成三维结构的模型,所述子模型为构成所述三维模型的子物件的模型。Understandably, the 3D file to be processed is an original 3D model file that has not been processed and needs surface reduction processing, and the process of constructing the 3D model data is to open the 3D file to be processed through application software For example, the application software is 3dsMax software, and the three-dimensional model data can be constructed by the 3dsMax software, and the three-dimensional model data is the data decoded and displayed by the application software, and the three-dimensional model data includes the three-dimensional model and the three-dimensional model Each of the sub-models below, the three-dimensional model is a model with a three-dimensional structure, and the sub-model is a model of the sub-objects constituting the three-dimensional model.
S20,对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值。S20. Perform surface reduction recognition on the 3D model data to obtain a surface reduction result and a reference surface reduction value corresponding to the surface reduction result.
可理解地,所述减面识别的处理方式可以根据需求进行设定,通过所述减面识别的处理过程可以识别出所述三维模型是否需要进行减面,即所述三维模型数据是否需要减面处理,以及在需减面的情况下,识别出基准减面值,在无需减面的情况下,将所述基准减面值设置为零,表明所述三维模型数据无需进行减面操作,说明所述三维模型数据已经达到无法减面的情况,如果再经过减面操作会出现渲染失真或者破坏三维模型的后果,所述减面结果表明了所述三维模型数据是否需要进行减面操作的结果,所述基准减面值为针对所述三维模型确定出的减面的基准值,比如所述减面识别的处理方式可以为对所述三维模型数据进行面数统计得到所述三维模型数据的总面数,根据总面数判断是否需要减面,如果需要减面,就结合各个所述子模型的体积值、表面积值和尺寸值确定出基准减面值的处理过程,所述减面识别的处理方式也可也为对所述三维模型数据进行平面数据收集,得到多个平面数据,通过减面识别模型对所有所述平面数据中的平面图像进行纹理特征提取,获取输出的减面结果,在需减面情况下,将各所述平面数据中的平面面数和平面尺寸进行向量转换成向量矩阵,通过基准识别模型对所述向量矩阵进行基准减面预测,预测出所述基准减面值的处理过程。Understandably, the processing method of the surface reduction recognition can be set according to requirements, through the processing process of the surface reduction recognition, it can be identified whether the 3D model needs to be reduced, that is, whether the 3D model data needs to be reduced. Face processing, and in the case of needing to reduce the face, identify the base face reduction value, in the case of no need to reduce the face value, set the reference face reduction value to zero, indicating that the three-dimensional model data does not need to be reduced. The 3D model data has already reached the situation where the surface reduction cannot be performed. If the surface reduction operation is performed again, rendering distortion or the consequence of destroying the 3D model will occur. The result of the surface reduction indicates whether the 3D model data needs to be reduced. The reference surface reduction value is the reference value of the surface reduction determined for the 3D model. For example, the processing method of the surface reduction recognition can be to perform surface number statistics on the 3D model data to obtain the total surface area of the 3D model data. According to the total number of faces, it is judged whether it is necessary to reduce the face. If it is necessary to reduce the face, the processing process of the reference face reduction value is determined in combination with the volume value, surface area value and size value of each of the sub-models. The processing method of the face reduction identification It is also possible to collect plane data on the three-dimensional model data to obtain a plurality of plane data, perform texture feature extraction on the plane images in all the plane data through the plane reduction recognition model, and obtain the output plane reduction results. In the case of face reduction, the number of faces and the size of the planes in each of the planar data are vector-converted into a vector matrix, and the reference recognition model is used to predict the reference face reduction of the vector matrix, and the process of predicting the value of the reference face reduction process.
其中,所述体积值为所述子模型的体积的值,所述表面积值为所述子模型的表面积的值,所述尺寸值为所述子模型的长宽高的值,所述平面数据为所述三维模型的空间形体的上、下、左、右、前、后六个方位的捕获下获得的数据,所述平面数据包括平面图像、平面面数和平面尺寸,所述平面图像为捕获的二值图像,即灰度化处理后的图像,所述平面面数为在一个方位捕获下的平面图像中的所有面数,所述平面尺寸为一个方位捕获下的平面图像中的三维模型的极限长和极限宽,所述极限长为捕获下的三维模型的最大长,所述极限宽为捕获下的三维模型的最大宽,所述平面尺寸包括所述极限长和所述极限宽。Wherein, the volume value is the value of the volume of the sub-model, the value of the surface area is the value of the surface area of the sub-model, the value of the size is the value of the length, width and height of the sub-model, and the plane data It is the data obtained under the capture of the upper, lower, left, right, front and rear six orientations of the spatial shape of the three-dimensional model. The plane data includes plane images, plane numbers and plane dimensions, and the plane images are The captured binary image, that is, the image after grayscale processing, the number of planes is the number of all planes in the plane image captured in one orientation, and the plane size is the three-dimensional dimension in the plane image captured in one orientation The limit length and limit width of the model, the limit length is the maximum length of the captured 3D model, the limit width is the maximum width of the captured 3D model, and the plane size includes the limit length and the limit width .
在一实施例中,所述步骤S20之后,即所述对所述三维模型进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值之后,还包括:S60,在所述减面结果为无需减面时,将所述三维模型数据压缩成减面三维文件并输出。In an embodiment, after the step S20, that is, after performing surface reduction recognition on the 3D model, obtaining a surface reduction result and a reference surface reduction value corresponding to the surface reduction result, it further includes: S60, after the When the result of surface reduction is that surface reduction is not required, the 3D model data is compressed into a 3D file with surface reduction and output.
可理解地,在所述三维模型无需减面的情况下,直接将所述三维模型数据压缩成所述减面三维文件,即运用LZMA(Lempel-Ziv-Markov chain-Algorithm)压缩算法,对所述三维模型数据进行压缩,得到所述减面三维文件,所述LZMA压缩算法使用了区间编码支持的LZ77(无损压缩算法)的改进压缩算法以及特殊用于二进制的预处理程序的算法。Understandably, in the case that the 3D model does not need to reduce the surface, the 3D model data is directly compressed into the 3D file with reduced surface, that is, the LZMA (Lempel-Ziv-Markov chain-Algorithm) compression algorithm is used to compress the The 3D model data is compressed to obtain the surface-reduced 3D file. The LZMA compression algorithm uses an improved compression algorithm of LZ77 (lossless compression algorithm) supported by interval coding and an algorithm specially used for binary preprocessing programs.
本发明实现了自动判断出减面结果为无需减面时,自动压缩三维模型数据得到减面三维文件,减少了人工自动识别及压缩的成本,自动压缩待处理三维文件,减小了文件大小。The present invention realizes automatically compressing the three-dimensional model data to obtain the reduced area three-dimensional file when it is automatically judged that the area reduction result is no need for area reduction, which reduces the cost of manual automatic identification and compression, automatically compresses the three-dimensional file to be processed, and reduces the file size.
在一实施例中,如图3所示,所述步骤S20中,即所述对所述三维模型进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值,包括:In one embodiment, as shown in FIG. 3, in the step S20, that is, performing surface reduction recognition on the 3D model, obtaining a surface reduction result and a reference surface reduction value corresponding to the surface reduction result, including:
S201,对所述三维模型数据进行面数统计,得到所述三维模型数据的总面数。S201. Perform face count statistics on the 3D model data to obtain the total face count of the 3D model data.
可理解地,所述面数统计为对所述三维模型中的各个所述子模型的面数进行统计的过程,所述总面数为经过所述面数统计处理后得到的面数总和,也即三维模型有多少个多边形就有多少个面数。Understandably, the statistics of the number of faces is a process of counting the number of faces of each of the sub-models in the three-dimensional model, and the total number of faces is the sum of the number of faces obtained after the statistical processing of the number of faces, That is, there are as many faces as there are polygons in the 3D model.
S202,判断所述总面数是否大于预设面数阈值。S202. Determine whether the total face count is greater than a preset face count threshold.
可理解地,所述预设面数阈值为预设的面数的值,所述预设面数阈值为设置的减面到最小的面数值,例预设面数阈值为8、10等等,判断所述总面数是否比所述预设面数阈值还小,如果所述总面数小于所述预设面数阈值,说明已经不能再减面,此时,无需进行减面。Understandably, the preset face number threshold is the value of the preset face number, and the preset face number threshold is the minimum face value set by reducing faces, for example, the preset face number threshold is 8, 10, etc. , judging whether the total number of sides is smaller than the preset number of sides threshold, if the total number of sides is smaller than the preset number of sides threshold, it means that the number of sides can no longer be reduced, and at this time, no need to reduce the number of sides.
S203,若所述总面数大于所述预设面数阈值,则将所述减面结果确定为需减面。S203. If the total number of faces is greater than the preset face number threshold, determine the result of face reduction as faces to be reduced.
可理解地,如果所述总面数大于所述预设面数阈值,就表明所述三维模型数据需要减面,即需对所述三维模型中的子模型需要减面,此时,将所述减面结果确定为需减面,所述减面结果包括需减面和无需减面。Understandably, if the total number of faces is greater than the preset face number threshold, it indicates that the 3D model data needs to be reduced, that is, the sub-models in the 3D model need to be reduced. At this time, the The area reduction result is determined as the area to be reduced, and the area reduction result includes the area to be reduced and the area not to be reduced.
S204,获取所述三维模型数据中的各所述子模型的体积值、表面积值和尺寸值。S204. Acquire the volume value, surface area value and size value of each of the sub-models in the three-dimensional model data.
可理解地,构建的所述三维模型数据中能够获取到所述三维模型中的各个所述子模型的所述体积值、所述表面积值和所述尺寸值。Understandably, the volume value, the surface area value, and the size value of each of the sub-models in the three-dimensional model can be obtained from the constructed three-dimensional model data.
S205,对所有所述子模型的所述体积值进行排序,得到第一排序结果,对所有所述子模型的所述表面积值进行排序,得到第二排序结果,对所有所述子模型的所述尺寸值进行排序,得到第三排序结果;S205, sort the volume values of all the sub-models to obtain a first sorting result, sort the surface area values of all the sub-models to obtain a second sorting result, and sort all the sub-models Sort the above size values to get the third sorting result;
可理解地,对各所述子模型的所述体积値进行由大到小的顺序进行排序,将排序后的所有所述体积值确定为所述第一排序结果,对各所述子模型的所述表面积值进行由大到小的顺序进行排序,将排序后的所有所述表面积值确定为所述第二排序结果,对各所述子模型的所述尺寸值进行有大到小的顺序进行排序,将排序后的所有所述尺寸值确定为所述第三排序结果。Understandably, the volume values of each of the sub-models are sorted in descending order, and all the volume values after sorting are determined as the first sorting result, and the volume values of each of the sub-models are determined as the first sorting result. The surface area values are sorted from large to small, and all the sorted surface area values are determined as the second sorting result, and the size values of each of the sub-models are sorted from large to small Sorting is performed, and all sorted size values are determined as the third sorting result.
S206,根据所述第一排序结果、所述第二排序结果和所述第三排序结果,确定与所述减面结果对应的所述基准减面值。S206. According to the first sorting result, the second sorting result, and the third sorting result, determine the reference face reduction value corresponding to the face reduction result.
可理解地,将所述第一排序结果中的序列靠前的前三的所述体积值和所述第一排序结果中的序列靠后的倒数三个所述体积值进行平均,得到第一排序均值,同理对所述第二排序结果和所述第三排序结果进行处理,分别得到第二排序均值和第三排序均值,对所述第一排序均值、所述第二排序均值和所述第三排序均值进行映射转换,转换成同一维度(即基准减面维度)的值,即检测所述第一排序均值落入哪一区段的数值范围,就将其映射成与该数值范围对应的第一基准值,同理对所述第二排序均值和所述第三排序均值做相同的映射转换处理,分别得到第二基准值和第三基准值,将所述第一基准值、所述第二基准值和所述第三基准值输入基准减面函数中,通过所述基准减面函数计算出所述基准减面值,所述基准减面函数为:Understandably, the first three volume values in the first sorting result and the last three volume values in the first sorting result are averaged to obtain the first A sorting mean value, the second sorting result and the third sorting result are processed similarly to obtain the second sorting mean value and the third sorting mean value respectively, and the first sorting mean value, the second sorting mean value and the The third sorting mean value is mapped and converted into a value of the same dimension (i.e., the base-reduction dimension), that is, the numerical range of which section the first sorting mean value falls into is detected, and it is mapped to the value corresponding to the numerical value. The first benchmark value corresponding to the range, similarly do the same mapping conversion process on the second sorting mean value and the third sorting mean value, respectively obtain the second benchmark value and the third benchmark value, and convert the first benchmark value , the second benchmark value and the third benchmark value are input into the benchmark face-reduction function, and the benchmark face-reduction value is calculated by the benchmark face-reduction function, and the benchmark face-reduction function is:
L=α1L1+α2L2+α3L3 L=α 1 L 1 +α 2 L 2 +α 3 L 3
其中:in:
L为基准减面值;L is the benchmark minus face value;
L1为第一基准值;L 1 is the first reference value;
L2为第二基准值;L 2 is the second reference value;
L3为第三基准值;L 3 is the third reference value;
α1为预设的第一基准权重;α 1 is the preset first benchmark weight;
α2为预设的第二基准权重;α 2 is the preset second benchmark weight;
α3为预设的第三基准权重。α 3 is a preset third benchmark weight.
本发明实现了通过对所述三维模型数据进行面数统计,得到所述三维模型数据的总面数;判断所述总面数是否大于预设面数阈值;若所述总面数大于所述预设面数阈值,则将所述减面结果确定为需减面;获取所述三维模型数据中的各所述子模型的体积值、表面积值和尺寸值;对所有所述子模型的所述体积值进行排序,得到第一排序结果,对所有所述子模型的所述表面积值进行排序,得到第二排序结果,对所有所述子模型的所述尺寸值进行排序,得到第三排序结果;根据所述第一排序结果、所述第二排序结果和所述第三排序结果,确定与所述减面结果对应的所述基准减面值,如此,实现了通过面数统计、预设面数阈值,自动判断是否需要进行减面,并通过对所述三维模型数据中的各所述子模型的体积值、表面积值和尺寸值进行排序,结合排序结果自动确定出基准减面值,减少人工操作,无需人工主观性的识别,提高了识别的准确性和可靠性,提高了三维模型减面的质量。The present invention realizes that the total number of faces of the three-dimensional model data is obtained by counting the number of faces of the three-dimensional model data; judging whether the total number of faces is greater than the preset face number threshold; if the total number of faces is greater than the If the threshold value of the number of faces is preset, then the result of the face reduction is determined as the face to be reduced; the volume value, the surface area value and the size value of each of the sub-models in the three-dimensional model data are obtained; Sort the volume values to obtain the first sorting result, sort the surface area values of all the sub-models to get the second sorting result, sort the size values of all the sub-models to get the third sorting result Result; according to the first sorting result, the second sorting result and the third sorting result, determine the benchmark denomination value corresponding to the deduction result, so that it is realized through face number statistics, preset Face number threshold, automatically judge whether need to reduce the face, and by sorting the volume value, surface area value and size value of each sub-model in the three-dimensional model data, combined with the sorting results to automatically determine the benchmark face value reduction, reduce Manual operation eliminates the need for manual subjective recognition, which improves the accuracy and reliability of recognition and improves the quality of surface reduction of 3D models.
在一实施例中,如图4所示,所述步骤S20中,即所述对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值,还包括:In one embodiment, as shown in FIG. 4, in the step S20, that is, performing surface reduction recognition on the 3D model data to obtain a surface reduction result and a reference surface reduction value corresponding to the surface reduction result, and include:
S207,对所述三维模型数据进行平面数据收集,得到多个平面数据;所述平面数据包括平面图像、平面面数和平面尺寸。S207. Collect plane data on the 3D model data to obtain a plurality of plane data; the plane data includes plane images, plane numbers and plane dimensions.
可理解地,所述平面数据收集为对所述三维模型数据进行六个面的捕获,得到所述三维模型数据在六个平面的所述平面图像;对各个所述平面图像进行面数汇总,得到与各所述平面图像对应的所述平面面数,同时对各个所述平面图像进行尺寸测量,得到与各所述平面图像对应的所述平面尺寸的处理过程。Understandably, the planar data collection is to capture six faces of the three-dimensional model data to obtain the planar images of the three-dimensional model data on six planes; to summarize the faces of each of the planar images, The process of obtaining the number of plane faces corresponding to each of the plane images, and simultaneously measuring the size of each of the plane images to obtain the plane size corresponding to each of the plane images.
在一实施例中,所述步骤S207中,即所述对所述三维模型数据进行平面数据收集,得到多个平面数据;所述平面数据包括平面图像、平面面数和平面尺寸,包括:In one embodiment, in the step S207, that is, collecting plane data on the 3D model data to obtain a plurality of plane data; the plane data includes plane images, plane numbers and plane dimensions, including:
S2071,对所述三维模型数据进行六个面的捕获,得到所述三维模型数据在六个平面的所述平面图像。S2071. Perform six-plane capture on the 3D model data to obtain the plane images of the 3D model data on six planes.
可理解地,所述捕获的过程为通过所述应用程序软件从空间形体的上、下、左、右、前、后六个方位进行捕获出所述平面图像的过程,也可理解为从上、下、左、右、前、后六个方位进行拍摄出图像然后进行二值化(即灰度化处理)后的图像的过程,所述二值化处理(即灰度化处理)是把含有亮度和色彩的彩色图像变化成灰度图像的过程。Understandably, the capture process is a process of capturing the plane image from six directions of the top, bottom, left, right, front and back of the spatial body through the application software, and it can also be understood as , down, left, right, front, and back six directions to take the image and then carry out the process of binarizing (ie, grayscale processing) the image, and the binarization process (ie, grayscale processing) is to The process of changing a color image containing brightness and color into a grayscale image.
S2072,对各个所述平面图像进行面数汇总,得到与各所述平面图像对应的所述平面面数,同时对各个所述平面图像进行尺寸测量,得到与各所述平面图像对应的所述平面尺寸。S2072. Summarize the number of faces of each of the planar images to obtain the number of faces of the plane corresponding to each of the planar images, and at the same time measure the size of each of the planar images to obtain the number of faces corresponding to each of the planar images Flat size.
可理解地,所述面数汇总为计算所述平面图像中的面数个数,从而得到该平面图像的平面面数,所述尺寸测量为测量所述平面图像中的平面维度的所述极限长和所述极限宽的过程。Understandably, the summary of the number of faces is to calculate the number of faces in the planar image, so as to obtain the number of planar faces of the planar image, and the measurement of the size is to measure the limit of the planar dimension in the planar image long and wide process of the limit.
S2073,将一个所述平面图像和与该平面图像对应的所述平面面数以及所述平面尺寸确定为一个所述平面数据。S2073. Determine one planar image, the number of plane faces and the plane size corresponding to the planar image as one piece of planar data.
可理解地,将一个方位的所述平面图像和与该平面图像对应的所述平面面数以及所述平面尺寸标记为该方位的所述平面数据。Understandably, the plane image of an orientation, the number of plane faces and the plane size corresponding to the plane image are marked as the plane data of the orientation.
S208,将所有所述平面图像输入减面识别模型,通过所述减面识别模型对所有所述平面图像进行纹理特征提取,获取所述减面识别模型根据提取的所述纹理特征识别出的所述减面结果。S208. Input all the planar images into the surface reduction recognition model, perform texture feature extraction on all the planar images through the surface reduction recognition model, and obtain all the texture features identified by the surface reduction recognition model based on the extracted texture features. Describe the surface reduction results.
可理解地,所述减面识别模型为训练完成的深度神经网络模型,所述减面识别模型的网络结构可以根据需求设定,优选为,所述减面识别模型的网络结构为VGG19的网络结构,所述减面识别模型为实现了通过提取所有所述平面图像中的所述纹理特征,并识别出是否需要减面的减面结果的模型,所述纹理特征为平面图像中多边形的边的长短、密度或者重合相关的特征,因为平面图像中的平面面数越多,纹理特征越明显,所以通过提取所述纹理特征能够更准确的确定出是否需要减面的减面结果,通过所述减面识别模型提取出的各所述平面图像中的所述纹理特征,并综合各所述平面图像中的所述纹理特征识别出所述减面结果。Understandably, the surface reduction recognition model is a trained deep neural network model, and the network structure of the surface reduction recognition model can be set according to requirements. Preferably, the network structure of the surface reduction recognition model is a network of VGG19 structure, the surface reduction recognition model is a model that realizes the surface reduction result by extracting the texture features in all the plane images and identifying whether surface reduction is required, and the texture features are polygonal edges in the plane image The length, density or coincidence-related features of the plane image, because the more planes in the plane image, the more obvious the texture features, so by extracting the texture features, it can be more accurately determined whether the surface reduction results need to be reduced. Through the The texture features in each of the planar images extracted by the surface reduction recognition model, and the texture features in each of the planar images are integrated to identify the result of surface reduction.
在一实施例中,所述步骤S208之前,即所述将所有所述平面图像输入减面识别模型之前,包括:In one embodiment, before the step S208, that is, before inputting all the plane images into the plane subtraction recognition model, it includes:
S2081,获取训练样本集;所述训练样本集包括多个训练样本;每个所述训练样本与一个减面标签关联;所述减面标签包括需减面和无需减面;一个所述训练样本包括一个历史收集的三维模型样本中的六个平面样本图像。S2081. Obtain a training sample set; the training sample set includes a plurality of training samples; each of the training samples is associated with a surface reduction label; the surface reduction label includes surface reduction and non-reduction; one training sample Includes six planar sample images from a historically collected 3D model sample.
可理解地,所述训练样本集为所述训练样本的集合,所述训练样本为历史收集的三维模型样本,所述三维模型样本为具有三维模型的数据作为样本,所述训练样本包括三维模型样本中的六个平面样本图像,所述减面标签表明了与其对应的所述训练样本是否需要减面的历史的结果,即所述减面标签为历史的所述训练样本是否进行过减面而赋予的标签,所述减面标签包括需减面和无需减面,一个所述训练样本与一个所述减面标签关联。Understandably, the training sample set is a collection of training samples, the training samples are historically collected 3D model samples, the 3D model samples are data with a 3D model as samples, and the training samples include 3D model samples. The six plane sample images in the sample, the surface reduction label indicates whether the training sample corresponding to it needs the result of surface reduction history, that is, whether the training sample whose surface reduction label is history has undergone surface reduction For the assigned label, the surface reduction label includes surface reduction and non-reduction surface, and one training sample is associated with one surface reduction label.
S2082,将所述训练样本输入包含初始参数的深度神经网络模型。S2082. Input the training sample into a deep neural network model including initial parameters.
可理解地,所述深度神经网络模型包括所述初始参数。Understandably, the deep neural network model includes the initial parameters.
S2083,通过所述深度神经网络模型提取所述训练样本中的所有所述平面样本图像的所述纹理特征。S2083. Extract the texture features of all the plane sample images in the training samples by using the deep neural network model.
S2084,获取所述深度神经网络模型根据提取的所述纹理特征输出的减面样本结果,并根据所述减面样本结果和所述减面标签的匹配程度确定损失值;S2084. Obtain a surface reduction sample result output by the deep neural network model according to the extracted texture feature, and determine a loss value according to a matching degree between the surface reduction sample result and the surface reduction label;
可理解地,所述深度神经网络模型根据提取的所述纹理特征进行识别,识别出所述训练样本是否需要减面,输出识别的结果作为所述减面样本结果,所述减面样本结果包括需减面和无需减面,所述减面样本结果表明了所述训练样本是否需要减面的结果,将与所述训练样本对应的所述减面样本结果和所述减面标签进行匹配,运用二分类交叉熵损失算法,可以匹配出两者之间的差异程度,从而计算出所述损失值,所述损失值衡量出所述减面样本结果和所述减面标签之间的差距,通过所述损失值可以不断让所述深度神经网络模型向准确的识别结果靠拢,提高识别的准确性。Understandably, the deep neural network model identifies according to the extracted texture features, identifies whether the training sample needs to be reduced, and outputs the result of recognition as the result of the reduced surface sample, and the reduced surface sample result includes face reduction is required and face reduction is not required, the face reduction sample result indicates whether the training sample needs face reduction results, and the face reduction sample result corresponding to the training sample is matched with the face reduction label, Using the binary classification cross-entropy loss algorithm, the degree of difference between the two can be matched, thereby calculating the loss value, which measures the gap between the surface-reduced sample result and the surface-reduced label, Through the loss value, the deep neural network model can be continuously moved closer to an accurate recognition result, thereby improving the recognition accuracy.
S2085,在所述损失值未达到预设的收敛条件时,迭代更新所述深度神经网络模型的初始参数,直至所述损失值达到所述预设的收敛条件时,将收敛之后的所述深度神经网络模型记录为训练完成的减面识别模型。S2085. When the loss value does not reach the preset convergence condition, iteratively update the initial parameters of the deep neural network model until the loss value reaches the preset convergence condition, and the depth after convergence The neural network model is recorded as the face reduction recognition model after training.
可理解地,所述收敛条件可以为所述损失值经过了1000次计算后值为很小且不会再下降的条件,即在所述损失值经过1000次计算后值为很小且不会再下降时,停止训练,并将收敛之后的所述深度神经网络模型记录为训练完成的减面识别模型;所述收敛条件也可以为所述损失值小于设定阈值的条件,即在所述损失值小于设定阈值时,停止训练,并将收敛之后的所述深度神经网络模型记录为训练完成的减面识别模型,如此,在所述损失值未达到预设的收敛条件时,不断调整所述深度神经网络模型的初始参数,并触发通过所述深度神经网络模型提取所述训练样本中的所有所述平面样本图像的所述纹理特征的步骤,可以不断向准确的结果靠拢,让识别的准确率越来越高。如此,能够优化减面识别,提高了三维模型减面识别的准确性和可靠性。Understandably, the convergence condition may be a condition that the loss value is very small and will not decrease after 1000 calculations, that is, the loss value is very small and will not decrease after 1000 calculations. When descending again, stop the training, and record the deep neural network model after the convergence as the face-reduction recognition model that the training is completed; When the loss value is less than the set threshold, the training is stopped, and the deep neural network model after convergence is recorded as the face-reduction recognition model that has been trained. In this way, when the loss value does not reach the preset convergence condition, it is continuously adjusted The initial parameters of the deep neural network model, and triggering the step of extracting the texture features of all the plane sample images in the training samples through the deep neural network model, can continuously move closer to accurate results, allowing recognition The accuracy rate is getting higher and higher. In this way, the surface reduction recognition can be optimized, and the accuracy and reliability of the 3D model surface reduction recognition are improved.
本发明实现了通过获取训练样本集;通过所述深度神经网络模型提取所述训练样本中的所有所述平面样本图像的所述纹理特征;获取所述深度神经网络模型根据提取的所述纹理特征输出的减面样本结果,并根据所述减面样本结果和所述减面标签的匹配程度确定损失值;在所述损失值未达到预设的收敛条件时,迭代更新所述深度神经网络模型的初始参数,直至所述损失值达到所述预设的收敛条件时,将收敛之后的所述深度神经网络模型记录为训练完成的减面识别模型,如此,本发明实现了通过历史的训练样本进行纹理特征的提取,并识别出减面样本结果,根据损失值不断迭代初始参数,实现了准确地、快速地识别出输入的图像是否需要减面的结果,能够提升三维模型减面识别的准确率和质量,减少了成本,提高了训练效率。The present invention realizes that by acquiring a training sample set; extracting the texture features of all the plane sample images in the training samples through the deep neural network model; acquiring the deep neural network model according to the extracted texture features output surface reduction sample results, and determine the loss value according to the matching degree of the surface reduction sample results and the surface reduction label; when the loss value does not reach the preset convergence condition, iteratively update the deep neural network model The initial parameters of the initial parameter, until the loss value reaches the preset convergence condition, the deep neural network model after convergence is recorded as the face-reduction recognition model that has been trained. In this way, the present invention realizes the Extract the texture features, and identify the surface reduction sample results, and continuously iterate the initial parameters according to the loss value, so as to realize the accurate and fast identification of whether the input image needs surface reduction results, which can improve the accuracy of 3D model surface reduction recognition The efficiency and quality are reduced, and the training efficiency is improved.
S209,在所述减面结果为需减面时,将各所述平面数据中的所述平面面数和所述平面尺寸进行向量转换,得到与所述三维模型数据对应的向量矩阵。S209. When the surface reduction result is that surface reduction is required, perform vector conversion on the number of plane surfaces and the plane size in each of the plane data to obtain a vector matrix corresponding to the three-dimensional model data.
可理解地,如果所述减面结果为需减面时,将各所述平面数据中的所述平面面数和所述平面尺寸进行向量转换,所述向量转换为将所述平面面数和所述平面尺寸转换成与其对应的同一维度(预设维度个数)的二进制码,例如都转换成16位二进制码,将一个所述平面数据中的所述平面面数和所述平面尺寸转换后的值组成一维的数组,将所有所述平面数据进行向量转换后组合成多维的数组,将该多维的数组确定为所述向量矩阵。Understandably, if the surface reduction result is that the surface needs to be reduced, the number of planes and the size of the planes in each of the plane data are converted into vectors, and the vector conversion is the number of planes and the number of planes The plane size is converted into a binary code corresponding to the same dimension (number of preset dimensions), such as a 16-bit binary code, and the plane number and the plane size in one plane data are converted The obtained values form a one-dimensional array, and all the planar data are combined into a multi-dimensional array after vector conversion, and the multi-dimensional array is determined as the vector matrix.
S210,将所述向量矩阵输入基准识别模型,通过所述基准识别模型对所述向量矩阵进行基准减面预测,得到与所述减面结果对应的所述基准减面值。S210. Input the vector matrix into a reference recognition model, and perform reference face reduction prediction on the vector matrix through the reference recognition model, to obtain the reference face reduction value corresponding to the result of the face reduction.
可理解地,所述基准识别模型为训练完成的聚类模型,所述基准识别模型通过历史收集的向量矩阵进行聚类,可以学习预测出基准减面值与向量矩阵之间的隐藏的映射关系,通过所述基准识别模型对所述向量矩阵进行基准减面预测,可以预测出与其对应的所述基准减面值。Understandably, the benchmark recognition model is a clustering model that has been trained, and the benchmark recognition model performs clustering through historically collected vector matrices, and can learn and predict the hidden mapping relationship between the benchmark minus face value and the vector matrix, The benchmark denomination prediction is performed on the vector matrix through the benchmark recognition model, and the corresponding benchmark denomination value can be predicted.
本发明实现了通过对所述三维模型数据进行平面数据收集,得到多个平面数据;将所有所述平面图像输入减面识别模型,通过所述减面识别模型对所有所述平面图像进行纹理特征提取,获取所述减面识别模型根据提取的所述纹理特征识别出的所述减面结果;在所述减面结果为需减面时,将各所述平面数据中的所述平面面数和所述平面尺寸进行向量转换,得到与所述三维模型数据对应的向量矩阵;将所述向量矩阵输入基准识别模型,通过所述基准识别模型对所述向量矩阵进行基准减面预测,得到与所述减面结果对应的所述基准减面值,如此,实现了通过对三维模型数据进行平面数据收集,以及通过减面识别模型提取纹理特征,能够识别出所述三维模型数据是否需要减面的减面结果,并且通过向量转换和基准识别模型能够预测出基准减面值,因此,能够准确地、科学地自动识别出是否需要减面的减面结果,而且能够快速预测出基准减面值,为后续三维模型的减面提供数据依据,提高了三维模型减面的识别准确率和质量。The present invention realizes that by collecting the plane data of the three-dimensional model data, a plurality of plane data are obtained; all the plane images are input into the surface reduction recognition model, and the texture features of all the plane images are carried out through the surface reduction recognition model Extracting, obtaining the surface reduction result identified by the surface reduction recognition model according to the extracted texture features; Carry out vector conversion with the plane size to obtain a vector matrix corresponding to the three-dimensional model data; input the vector matrix into the benchmark recognition model, and perform benchmark reduction area prediction on the vector matrix through the benchmark recognition model to obtain the same as The reference denomination value corresponding to the surface reduction result, in this way, it is possible to identify whether the 3D model data needs surface reduction by collecting plane data on the 3D model data and extracting texture features through the surface reduction recognition model. Therefore, it can accurately and scientifically automatically identify whether the result of face reduction is needed, and can quickly predict the benchmark denomination value for subsequent The surface reduction of the 3D model provides a data basis, which improves the recognition accuracy and quality of the surface reduction of the 3D model.
S30,在所述减面结果为需减面时,根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值。S30. When the surface reduction result is that surface reduction is required, determine a target face value corresponding to each sub-model in the three-dimensional model data according to the reference face reduction value.
可理解地,在所述减面结果为需减面时,结合所述基准减面值,以及所述三维模型数据中的各所述子模型的体积值、体积占比值、表面积值、表面积占比值、尺寸值和尺寸占比值计算出所述三维模型数据中的各个子模型的所述目标面数值,一个所述子模型的所述目标面数值表明了该子模型需要减面达到的面数的值。Understandably, when the surface reduction result is surface reduction, combined with the reference surface reduction value and the volume value, volume ratio, surface area value, and surface area ratio of each of the sub-models in the three-dimensional model data , size value and size ratio to calculate the target surface value of each sub-model in the three-dimensional model data, and the target surface value of a sub-model indicates the number of surfaces that the sub-model needs to reduce to achieve. value.
在一实施例中,如图5所示,所述步骤S30中,即所述根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值,包括:In one embodiment, as shown in FIG. 5, in the step S30, that is, determining the target denomination corresponding to each sub-model in the three-dimensional model data according to the denomination of the reference, including:
S301,获取所述三维模型数据中的各所述子模型的体积值、体积占比值、表面积值、表面积占比值、尺寸值和尺寸占比值。S301. Obtain volume values, volume proportions, surface areas, surface area proportions, dimensions, and dimension proportions of each of the sub-models in the three-dimensional model data.
可理解地,所述三维模型数据还包括各所述子模型的所述体积占比值、所述表面积占比值和所述尺寸占比值,所述体积占比值为所述子模型的体积值占所述三维模型的总体积的百分比,所述表面积占比值为所述子模型的表面积值占所述三维模型的总表面积的百分比,所述尺寸占比值为所述子模型的尺寸值占所述三维模型的总尺寸的百分比,即各子模型的极限长和极限宽的乘积占所述三维模型的长和宽的乘积的百分比。Understandably, the three-dimensional model data also includes the volume ratio, the surface area ratio and the size ratio of each of the sub-models, and the volume ratio is the volume ratio of the sub-models. The percentage of the total volume of the three-dimensional model, the surface area ratio is the percentage of the surface area value of the sub-model to the total surface area of the three-dimensional model, and the size ratio is the ratio of the size value of the sub-model to the three-dimensional The percentage of the total size of the model, that is, the percentage of the product of the limit length and limit width of each sub-model to the product of the length and width of the three-dimensional model.
S302,根据各所述子模型的所述体积值、所述表面积值和所述尺寸值,确定出与各所述子模型对应的保护面数值。S302. Determine, according to the volume value, the surface area value, and the size value of each of the sub-models, a protective surface value corresponding to each of the sub-models.
可理解地,根据所述子模型的所述体积值、所述表面积值和所述尺寸值,可以映射出与其对应的所述保护面数值,所述保护面数值为保证子模型的轮廓不被减面破坏的面数,即所述体积值、所述表面积值和所述尺寸值分别落入哪一区段,然后根据分别落入的三个区段映射出一个保护面数值,其中,三个维度的区段与一个保护面数值映射对应。Understandably, according to the volume value, the surface area value and the size value of the sub-model, the value of the protection surface corresponding thereto can be mapped out, and the value of the protection surface is to ensure that the outline of the sub-model is not The number of surfaces damaged by surface reduction, that is, which section the volume value, the surface area value and the size value fall into respectively, and then map a protection surface value according to the three sections falling into it respectively, among which three A segment of dimensions corresponds to a protection surface value map.
S303,将所述基准减面值与各所述子模型的所述体积占比值相乘,得到与各所述子模型对应的第一占比值,将所述基准减面值与各所述子模型的所述表面积占比值相乘,得到与各所述子模型对应的第二占比值,将所述基准减面值与各所述子模型的所述尺寸占比值相乘,得到与各所述子模型对应的第三占比值。S303. Multiply the base denomination value by the volume ratio of each of the sub-models to obtain a first proportion value corresponding to each of the sub-models, and multiply the reference denomination value by the volume proportion of each of the sub-models multiplying the surface area proportions to obtain a second proportion corresponding to each of the sub-models, multiplying the base minus face value by the size proportions of each of the sub-models to obtain a second proportion corresponding to each of the sub-models The corresponding third proportion value.
S304,根据与相同的所述子模型对应的所述保护面数值、所述第一占比值、所述第二占比值和所述第三占比值,确定出与该子模型对应的所述目标面数值。S304. Determine the target corresponding to the sub-model according to the protection surface value, the first proportion, the second proportion, and the third proportion corresponding to the same sub-model face value.
可理解地,通过与所述子模型对应的所述第一占比值、所述第二占比值和所述第三占比值,确定出与所述子模型对应的预估面数值,通过与所述子模型对应的所述预估面数值与所述保护面数值进行比较,从而确定出与该子模型对应的所述目标面数值。Understandably, the estimated face value corresponding to the sub-model is determined through the first proportion value, the second proportion value and the third proportion value corresponding to the sub-model, and by combining with the The predicted face value corresponding to the sub-model is compared with the protected face value, so as to determine the target face value corresponding to the sub-model.
本发明实现了通过获取所述三维模型数据中的各所述子模型的体积值、体积占比值、表面积值、表面积占比值、尺寸值和尺寸占比值;根据各所述子模型的所述体积值、所述表面积值和所述尺寸值,确定出与各所述子模型对应的保护面数值;将所述基准减面值与各所述子模型的所述体积占比值相乘,得到与各所述子模型对应的第一占比值,将所述基准减面值与各所述子模型的所述表面积占比值相乘,得到与各所述子模型对应的第二占比值,将所述基准减面值与各所述子模型的所述尺寸占比值相乘,得到与各所述子模型对应的第三占比值;根据与相同的所述子模型对应的所述保护面数值、所述第一占比值、所述第二占比值和所述第三占比值,确定出与该子模型对应的所述目标面数值,如此,实现了科学地和准确地自动确定出与各个子模型对应的目标面数值,能够保证了各个子模型进行减面的质量,提供了各个子模型的减面的目标面数值,为后续的减面提高了准确率,并避免了减面的失真和破坏。The present invention realizes that by acquiring the volume value, volume ratio, surface area value, surface area ratio, size value and size ratio of each of the sub-models in the three-dimensional model data; according to the volume of each of the sub-models value, the surface area value and the size value, determine the protective surface value corresponding to each of the sub-models; multiply the base minus face value with the volume ratio of each of the sub-models to obtain the corresponding For the first proportion corresponding to the sub-models, multiply the base minus face value by the surface area proportions of each of the sub-models to obtain a second proportion corresponding to each of the sub-models, and multiply the base The deduction face value is multiplied by the size ratio of each of the sub-models to obtain the third ratio corresponding to each of the sub-models; according to the protection face value corresponding to the same sub-model, the first A proportion value, the second proportion value and the third proportion value determine the target denomination value corresponding to the sub-model, so that scientifically and accurately automatically determine the denomination value corresponding to each sub-model The target surface value can ensure the quality of surface reduction for each sub-model, and provides the target surface value of each sub-model for surface reduction, which improves the accuracy of subsequent surface reduction and avoids distortion and destruction of surface reduction.
在一实施例中,所述步骤S304中,即所述根据与相同的所述子模型对应的所述保护面数值、所述第一占比值、所述第二占比值和所述第三占比值,确定出与该子模型对应的所述目标面数值,包括:In one embodiment, in the step S304, that is, according to the value of the protection area, the first proportion value, the second proportion value and the third proportion value corresponding to the same sub-model Ratio, determine the target face value corresponding to the sub-model, including:
S3041,将与所述子模型对应的所述第一占比值、所述第二占比值和所述第三占比值中的最大的值进行取整,将其确定为与该子模型对应的预估面数值。S3041. Round up the largest value among the first proportion value, the second proportion value, and the third proportion value corresponding to the sub-model, and determine it as the predicted value corresponding to the sub-model. face value.
可理解地,所述取整为对所述第一占比值、所述第二占比值和所述第三占比值之中的最大的值进行向下取整,将取整后的值确定为该子模型对应的所述预估面数值,所述预估面数值为预估该子模型需要减面达到的面数的值。Understandably, the rounding is rounding down the largest value among the first percentage value, the second percentage value and the third percentage value, and the rounded value is determined as The estimated face value corresponding to the sub-model, the estimated face value is the estimated face number that the sub-model needs to reduce to achieve.
S3042,将与所述子模型对应的所述预估面数值和所述保护面数值进行比较。S3042. Compare the predicted face value corresponding to the sub-model with the protected face value.
S3043,若与所述子模型对应的所述预估面数值小于或等于所述保护面数值,则将所述保护面数值确定为与该子模型对应的所述目标面数值。S3043. If the predicted face value corresponding to the sub-model is less than or equal to the protected face value, determine the protected face value as the target face value corresponding to the sub-model.
S3044,若与所述子模型对应的所述预估面数值大于所述保护面数值,则将所述预估面数值确定为与该子模型对应的所述目标面数值。S3044. If the estimated face value corresponding to the sub-model is greater than the protected face value, determine the estimated face value as the target face value corresponding to the sub-model.
本发明实现了通过将与所述子模型对应的所述第一占比值、所述第二占比值和所述第三占比值中的最大的值进行取整,将其确定为与该子模型对应的预估面数值;将与所述子模型对应的所述预估面数值和所述保护面数值进行比较;若与所述子模型对应的所述预估面数值小于或等于所述保护面数值,则将所述保护面数值确定为与该子模型对应的所述目标面数值;若与所述子模型对应的所述预估面数值大于所述保护面数值,则将所述预估面数值确定为与该子模型对应的所述目标面数值,如此,提供了一种确定各个子模型的目标面数值的方法,提供了后续三维模型减面的目标,提高了三维模型减面的质量。The present invention realizes that by rounding the largest value among the first proportion value, the second proportion value and the third proportion value corresponding to the sub-model, it is determined to be the same as the sub-model Corresponding estimated face value; compare the estimated face value corresponding to the sub-model with the protected face value; if the estimated face value corresponding to the sub-model is less than or equal to the protected face value face value, then determine the protected face value as the target face value corresponding to the sub-model; if the estimated face value corresponding to the sub-model is greater than the protected face value, then set the predicted face value The estimated surface value is determined as the target surface value corresponding to the sub-model, thus, a method for determining the target surface value of each sub-model is provided, the target of subsequent three-dimensional model reduction is provided, and the three-dimensional model reduction is improved. the quality of.
S40,根据所有与所述子模型对应的目标面数值,对所述三维模型数据做相应的减面。S40. According to all the target surface values corresponding to the sub-models, corresponding surface reduction is performed on the 3D model data.
可理解地,通过所述应用程序根据与各个所述子模型的所述目标面数值对各个所述子模型进行减面,能够自动做相应的减面操作,将不符合规则和不必要的显示面进行减去,让各个子模型保留的面数向与其对应的所述目标面数值靠近,直至达到目标面数值,从而完成相应的减面操作,最终所有所述子模型的相应减面操作完成后,得到减面后的所述三维模型数据。Understandably, through the application program to reduce the area of each of the sub-models according to the target area value of each of the sub-models, the corresponding area reduction operation can be automatically performed, and the non-compliant and unnecessary display Surfaces are subtracted, so that the number of surfaces retained by each sub-model approaches the corresponding target surface value until the target surface value is reached, thereby completing the corresponding surface reduction operation, and finally the corresponding surface reduction operations of all the sub-models are completed After that, the 3D model data after surface reduction is obtained.
S50,将减面后的所述三维模型数据压缩成减面三维文件并输出。S50. Compress the 3D model data after plane reduction into a plane-reduced 3D file and output it.
可理解地,将减面后的所述三维模型数据压缩成所述减面三维文件,即运用LZMA(Lempel-Ziv-Markov chain-Algorithm)压缩算法,对减面后的所述三维模型数据进行压缩,得到所述减面三维文件,所述LZMA压缩算法使用了区间编码支持的LZ77(无损压缩算法)的改进压缩算法以及特殊用于二进制的预处理程序的算法,将所述减面三维文件输出,完成该待处理三维文件的减面过程,通过实验数据,所述减面三维文件保证了后续渲染的效果,所述减面三维文件的容量比所述待处理三维文件的容量少10%以上。Understandably, compressing the 3D model data after plane reduction into the plane-reduced 3D file, that is, using the LZMA (Lempel-Ziv-Markov chain-Algorithm) compression algorithm to compress the 3D model data after plane reduction Compress to obtain the reduced plane three-dimensional file, the LZMA compression algorithm uses the improved compression algorithm of LZ77 (lossless compression algorithm) supported by interval coding and the algorithm specially used for the binary preprocessing program, and the described reduced plane three-dimensional file Output, complete the surface reduction process of the 3D file to be processed, through the experimental data, the 3D file with reduced surface ensures the effect of subsequent rendering, and the capacity of the 3D file with reduced surface is 10% less than the capacity of the 3D file to be processed above.
本发明实现了通过获取待处理三维文件,构建与所述待处理三维文件对应的三维模型数据;对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值;在所述减面结果为需减面时,根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值;根据所有与所述子模型对应的目标面数值,对所述三维模型数据做相应的减面;将减面后的所述三维模型数据压缩成减面三维文件并输出,如此,实现了通过构建三维模型数据,自动识别减面结果和基准减面值,在减面结果为需减面时,自动输出三维模型数据中的各子模型的目标面数值,并对各子模型减面按与其对应的目标面数值进行相应减面,并压缩输出减面三维文件,达到自动完成三维模型的减面操作,无需人工操作,减少了人工成本,并提高了三维模型减面的效率,以及保证了三维模型减面的有效性和不失真,提高了三维模型减面的质量。The present invention achieves the construction of three-dimensional model data corresponding to the three-dimensional file to be processed by acquiring the three-dimensional file to be processed; performing surface reduction recognition on the three-dimensional model data to obtain the surface reduction result and the benchmark corresponding to the surface reduction result denomination; when the result of the deduction is that the denomination is required, according to the benchmark denomination, determine the target denomination corresponding to each sub-model in the three-dimensional model data; according to all the targets corresponding to the sub-models surface value, corresponding surface reduction is performed on the three-dimensional model data; the three-dimensional model data after surface reduction is compressed into a surface-reduction three-dimensional file and output, so that by building three-dimensional model data, automatic recognition of surface reduction results and Base face value reduction, when the face reduction result is the face reduction, the target face value of each sub-model in the 3D model data is automatically output, and the face reduction of each sub-model is correspondingly reduced according to the corresponding target face value, and compressed Output the 3D surface reduction file to automatically complete the surface reduction operation of the 3D model without manual operation, reduce labor costs, improve the efficiency of 3D model surface reduction, and ensure the effectiveness and undistortion of the 3D model surface reduction, improving Improve the quality of the 3D model minus the surface.
在一实施例中,提供一种三维模型的减面装置,该三维模型的减面装置与上述实施例中三维模型的减面方法一一对应。如图6所示,该三维模型的减面装置包括获取模块11、识别模块12、确定模块13、减面模块14和输出模块15。各功能模块详细说明如下:In one embodiment, a device for reducing the surface of a three-dimensional model is provided, and the device for reducing the surface of the three-dimensional model corresponds to the method for reducing the surface of the three-dimensional model in the above-mentioned embodiments. As shown in FIG. 6 , the surface reduction device for the 3D model includes an acquisition module 11 , an identification module 12 , a determination module 13 , a surface reduction module 14 and an output module 15 . The detailed description of each functional module is as follows:
获取模块11,用于获取待处理三维文件,构建与所述待处理三维文件对应的三维模型数据;An acquisition module 11, configured to acquire a three-dimensional file to be processed, and construct three-dimensional model data corresponding to the three-dimensional file to be processed;
识别模块12,用于对所述三维模型数据进行减面识别,得到减面结果以及与所述减面结果对应的基准减面值;The identification module 12 is configured to perform area reduction identification on the three-dimensional model data, and obtain an area reduction result and a reference area reduction value corresponding to the area reduction result;
确定模块13,用于在所述减面结果为需减面时,根据所述基准减面值,确定与所述三维模型数据中的各子模型对应的目标面数值;The determination module 13 is used to determine the target denomination corresponding to each sub-model in the three-dimensional model data according to the reference denomination denomination when the deduction result is denomination required;
减面模块14,用于根据所有与所述子模型对应的目标面数值,对所述三维模型数据做相应的减面;A surface reduction module 14, configured to perform corresponding surface reduction on the three-dimensional model data according to all target surface values corresponding to the sub-models;
输出模块15,用于将减面后的所述三维模型数据压缩成减面三维文件并输出。The output module 15 is configured to compress the 3D model data after plane reduction into a plane-reduced 3D file and output it.
关于三维模型的减面装置的具体限定可以参见上文中对于三维模型的减面方法的限定,在此不再赘述。上述三维模型的减面装置中的各个模块可全部或部分通过软件、硬件及其组合来实现。上述各模块可以硬件形式内嵌于或独立于计算机设备中的处理器中,也可以以软件形式存储于计算机设备中的存储器中,以便于处理器调用执行以上各个模块对应的操作。For the specific limitations of the surface reduction device for a 3D model, refer to the above definition of the surface reduction method for a 3D model, which will not be repeated here. Each module in the above-mentioned three-dimensional model surface reduction device can be fully or partially realized by software, hardware and a combination thereof. The above-mentioned modules can be embedded in or independent of the processor in the computer device in the form of hardware, and can also be stored in the memory of the computer device in the form of software, so that the processor can invoke and execute the corresponding operations of the above-mentioned modules.
在一个实施例中,提供了一种计算机设备,该计算机设备可以是服务器,其内部结构图可以如图7所示。该计算机设备包括通过系统总线连接的处理器、存储器、网络接口和数据库。其中,该计算机设备的处理器用于提供计算和控制能力。该计算机设备的存储器包括非易失性存储介质、内存储器。该非易失性存储介质存储有操作系统、计算机程序和数据库。该内存储器为非易失性存储介质中的操作系统和计算机程序的运行提供环境。该计算机设备的网络接口用于与外部的终端通过网络连接通信。该计算机程序被处理器执行时以实现一种三维模型的减面方法。In one embodiment, a computer device is provided. The computer device may be a server, and its internal structure may be as shown in FIG. 7 . The computer device includes a processor, memory, network interface and database connected by a system bus. Wherein, the processor of the computer device is used to provide calculation and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system, computer programs and databases. The internal memory provides an environment for the operation of the operating system and computer programs in the non-volatile storage medium. The network interface of the computer device is used to communicate with an external terminal via a network connection. When the computer program is executed by a processor, a method for reducing the surface of a three-dimensional model is realized.
在一个实施例中,提供了一种计算机设备,包括存储器、处理器及存储在存储器上并可在处理器上运行的计算机程序,处理器执行计算机程序时实现上述实施例中三维模型的减面方法。In one embodiment, a computer device is provided, including a memory, a processor, and a computer program stored on the memory and operable on the processor. When the processor executes the computer program, the surface reduction of the three-dimensional model in the above-mentioned embodiments is realized. method.
在一个实施例中,提供了一种计算机可读存储介质,其上存储有计算机程序,计算机程序被处理器执行时实现上述实施例中三维模型的减面方法。In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, and when the computer program is executed by a processor, the method for reducing the surface of a three-dimensional model in the above-mentioned embodiments is implemented.
本领域普通技术人员可以理解实现上述实施例方法中的全部或部分流程,是可以通过计算机程序来指令相关的硬件来完成,所述的计算机程序可存储于一非易失性计算机可读取存储介质中,该计算机程序在执行时,可包括如上述各方法的实施例的流程。其中,本发明所提供的各实施例中所使用的对存储器、存储、数据库或其它介质的任何引用,均可包括非易失性和/或易失性存储器。非易失性存储器可包括只读存储器(ROM)、可编程ROM(PROM)、电可编程ROM(EPROM)、电可擦除可编程ROM(EEPROM)或闪存。易失性存储器可包括随机存取存储器(RAM)或者外部高速缓冲存储器。作为说明而非局限,RAM以多种形式可得,诸如静态RAM(SRAM)、动态RAM(DRAM)、同步DRAM(SDRAM)、双数据率SDRAM(DDRSDRAM)、增强型SDRAM(ESDRAM)、同步链路(Synchlink)DRAM(SLDRAM)、存储器总线(Rambus)直接RAM(RDRAM)、直接存储器总线动态RAM(DRDRAM)、以及存储器总线动态RAM(RDRAM)等。Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be implemented through computer programs to instruct related hardware, and the computer programs can be stored in a non-volatile computer-readable memory In the medium, when the computer program is executed, it may include the processes of the embodiments of the above-mentioned methods. Wherein, any reference to memory, storage, database or other media used in the various embodiments provided by the present invention may include non-volatile and/or volatile memory. Nonvolatile memory can include read only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in many forms such as Static RAM (SRAM), Dynamic RAM (DRAM), Synchronous DRAM (SDRAM), Double Data Rate SDRAM (DDRSDRAM), Enhanced SDRAM (ESDRAM), Synchronous Chain Synchlink DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.
所属领域的技术人员可以清楚地了解到,为了描述的方便和简洁,仅以上述各功能单元、模块的划分进行举例说明,实际应用中,可以根据需要而将上述功能分配由不同的功能单元、模块完成,即将所述装置的内部结构划分成不同的功能单元或模块,以完成以上描述的全部或者部分功能。Those skilled in the art can clearly understand that for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used for illustration. In practical applications, the above-mentioned functions can be assigned to different functional units, Completion of modules means that the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above.
以上所述实施例仅用以说明本发明的技术方案,而非对其限制;尽管参照前述实施例对本发明进行了详细的说明,本领域的普通技术人员应当理解:其依然可以对前述各实施例所记载的技术方案进行修改,或者对其中部分技术特征进行等同替换;而这些修改或者替换,并不使相应技术方案的本质脱离本发明各实施例技术方案的精神和范围,均应包含在本发明的保护范围之内。The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that: it can still carry out the foregoing embodiments Modifications to the technical solutions recorded in the examples, or equivalent replacement of some of the technical features; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should be included in within the protection scope of the present invention.
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