In this paper, we address the problem of 3D object mesh reconstruction from
RGB videos. Our approach combines the best of multi-view geometric and
data-driven methods for 3D reconstruction by optimizing object meshes for
multi-view photometric consistency while constraining mesh deformations with a
shape prior. We pose this as a piecewise image alignment problem for each mesh
face projection. Our approach allows us to update shape parameters from the
photometric error without any depth or mask information. Moreover, we show how
to avoid a degeneracy of zero photometric gradients via rasterizing from a
virtual viewpoint. We demonstrate 3D object mesh reconstruction results from
both synthetic and real-world videos with our photometric mesh optimization,
which is unachievable with either na\"ive mesh generation networks or
traditional pipelines of surface reconstruction without heavy manual
post-processing.

本论文研究了从 RGB 视频中重建三维物体网格的方法，结合多视角几何和数据驱动方法来优化物体网格以适应多视角光度一致性，并通过形状先验来约束网格变形。作者采用逐片图像对齐的形式，通过光度误差更新形状参数，无需深度或掩膜信息。此外，作者还展示了如何通过虚拟视点的光栅化来避免零光度梯度导致的退化问题，并展示了采用光度网格优化的方法从合成和真实视频中获得的三维物体网格重建结果，相比于传统的网格生成网络或表面重建方法，避免了繁琐的手动后处理。