3D reconstruction is an important part of digital city, high-accuracy 3D modeling method has been widely studied as an important pathway to visualizing 3D city scenes. However, the problems of image resolution, noise, and occlusion result in low quality and smooth features in the mesh model. Therefore, the model needs to be refined to improve the mesh quality and enhance the visual effect. This paper proposes a mesh refinement algorithm to fine-tune the vertices of the mesh and constrain their evolution direction on the normal vector, reducing their freedom degrees to one. The evolution of vertices only involves one motion distance parameter on the normal vector, simplifying the complexity of the energy function derivation. Meanwhile, Gaussian curvature is used as a regularization term, which is anisotropic and preserves the edge features during the reconstruction process. The mesh refinement algorithm with unary operations fully utilizes the original image information and effectively enriches the local detail features of the mesh model. This paper utilizes five public datasets to conduct comparative experiments, and the experimental results show that the proposed algorithm can better restore the detailed features of the model and has a better refinement effect in the same number of iterations compared with OpenMVS library refinement algorithm. At the same time, in the comparison of refinement results with fewer iterations, the algorithm in this paper can achieve more desirable results.

中文翻译：

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具有一元操作的多视图立体的网格细化方法


3D 重建是数字城市的重要组成部分，高精度 3D 建模方法作为可视化 3D 城市场景的重要途径已被广泛研究。然而，图像分辨率、噪声和遮挡等问题导致网格模型中的特征质量低下且特征平滑。因此，需要对模型进行细化，以提高网格质量，增强视觉效果。本文提出了一种网格细化算法，用于微调网格的顶点，并约束它们在法向量上的演化方向，将其自由度减少到 1。顶点的演变只涉及法线向量上的一个运动距离参数，简化了能量函数推导的复杂性。同时，高斯曲率被用作正则化项，它是各向异性的，在重建过程中保留了边缘特征。一元运算的网格细化算法充分利用了原始图像信息，有效地丰富了网格模型的局部细节特征。本文利用 5 个公共数据集进行了对比实验，实验结果表明，与 OpenMVS 库细化算法相比，所提算法能更好地还原模型的细节特征，并且在相同迭代次数下具有更好的细化效果。同时，在迭代次数较少的细化结果的比较中，本文中的算法可以获得更理想的结果。