用稀疏三角网格压缩法线图，加速3D重建同时保持精度。
Feature-Preserving Mesh Decimation for Normal Integration
- 用自适应稀疏三角网格替代密集像素，根据几何复杂度调整采样密度。
- 高分辨率下重建时间从数小时缩短至分钟级，表面误差小于0.15mm。
- 适合需要快速高精度3D重建的工业扫描与动画建模场景。
法线积分用于从光度立体等方法获得的法线图重建三维表面。这些法线图能捕捉到像素级的表面细节，但高分辨率下的积分计算量巨大。本文在法线积分前，将密集像素网格替换为稀疏的各向异性三角网格，根据局部几何复杂度自适应调整网格密度，在复杂结构区域保留细节，在平坦无特征区域去除冗余采样。对于高分辨率图像，该压缩方法使法线积分运行时间从数小时降至几分钟，同时保持高表面精度（误差低于0.15mm）。主要贡献是将经典的二次误差度量（quadric error measure）从网格简化中推导并适配于屏幕空间应用，并结合最优Delaunay三角剖分实现高效重构。
原文摘要 · Abstract (English)
Normal integration reconstructs 3D surfaces from normal maps obtained e.g. by photometric stereo. These normal maps capture surface details down to the pixel level but require large computational resources for integration at high resolutions. In this work, we replace the dense pixel grid with a sparse anisotropic triangle mesh prior to normal integration. We adapt the triangle mesh to the local geometry in the case of complex surface structures and remove oversampling from flat featureless regions. For high-resolution images, the resulting compression reduces normal integration runtimes from hours to minutes while maintaining high surface accuracy. Our main contribution is the derivation of the well-known quadric error measure from mesh decimation for screen space applications and its combination with optimal Delaunay triangulation.
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