The Markov Random Field (MRF) energy function, constructed by existing OpenMVS-based 3D texture reconstruction algorithms, considers only the image label of the adjacent triangle face for the smoothness term and ignores the planar-structure information of the model. As a result, the generated texture charts results have too many fragments, leading to a serious local miscut and color discontinuity between texture charts. This paper fully utilizes the planar structure information of the mesh model and the visual information of the 3D triangle face on the image and proposes an improved, faster, and high-quality texture chart generation method based on the texture chart generation algorithm of the OpenMVS. This methodology of the proposed approach is as follows: (1) The visual quality on different visual images of each triangle face is scored using the visual information of the triangle face on each image in the mesh model. (2) A fully automatic Variational Shape Approximation (VSA) plane segmentation algorithm is used to segment the blocked 3D mesh models. The proposed fully automatic VSA-based plane segmentation algorithm is suitable for multi-threaded parallel processing, which solves the VSA framework needed to manually set the number of planes and the low computational efficiency in a large scene model. (3) The visual quality of the triangle face on different visual images is used as the data term, and the image label of adjective triangle and result of plane segmentation are utilized as the smoothness term to construct the MRF energy function. (4) An image label is assigned to each triangle by the minimizing energy function. A texture chart is generated by clustering the topologically-adjacent triangle faces with the same image label, and the jagged boundaries of the texture chart are smoothed. Three sets of data of different types were used for quantitative and qualitative evaluation. Compared with the original OpenMVS texture chart generation method, the experiments show that the proposed approach significantly reduces the number of texture charts, significantly improves miscuts and color differences between texture charts, and highly boosts the efficiency of VSA plane segmentation algorithm and OpenMVS texture reconstruction.

中文翻译：

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基于全自动平面分割的3D网格模型基于OpenMVS的纹理重构新方法

由现有的基于OpenMVS的3D纹理重构算法构造的Markov随机场（MRF）能量函数仅考虑相邻三角形面的图像标签作为平滑度项，而忽略了模型的平面结构信息。结果，生成的纹理图结果有太多碎片，导致纹理图之间严重的局部误切和颜色不连续。本文充分利用了网格模型的平面结构信息和图像上3D三角面的视觉信息，并基于OpenMVS的纹理图生成算法提出了一种改进，更快，高质量的纹理图生成方法。提议的方法的这种方法如下：（1）使用网格模型中的每个图像上的三角形面的视觉信息对每个三角形面的不同视觉图像上的视觉质量进行评分。（2）全自动变异形状近似（VSA）平面分割算法用于分割已阻止的3D网格模型。所提出的基于VSA的全自动平面分割算法适用于多线程并行处理，解决了在大型场景模型中手动设置平面数所需的VSA框架和低计算效率的问题。（3）将不同视觉图像上的三角形人脸的视觉质量作为数据项，将形容词三角形的图像标签和平面分割结果作为平滑项来构造MRF能量函数。（4）通过最小化能量函数将图像标签分配给每个三角形。通过将拓扑相邻的三角形面与相同的图像标签聚类来生成纹理图，并平滑纹理图的锯齿状边界。使用三组不同类型的数据进行定量和定性评估。与原始的OpenMVS纹理图生成方法相比，实验表明该方法显着减少了纹理图的数量，显着改善了纹理图之间的误切和色差，并大大提高了VSA平面分割算法和OpenMVS纹理重建的效率。并且平滑了纹理图的锯齿状边界。使用三组不同类型的数据进行定量和定性评估。与原始的OpenMVS纹理图生成方法相比，实验表明该方法显着减少了纹理图的数量，显着改善了纹理图之间的误切和色差，并大大提高了VSA平面分割算法和OpenMVS纹理重建的效率。并且平滑了纹理图的锯齿状边界。使用三组不同类型的数据进行定量和定性评估。与原始的OpenMVS纹理图生成方法相比，实验表明该方法显着减少了纹理图的数量，显着改善了纹理图之间的误切和色差，并大大提高了VSA平面分割算法和OpenMVS纹理重建的效率。