Colmap学习笔记（一）：Pixelwise View Selection for Unstructured Multi-View Stereo 论文阅读

1. 摘要

本文展示一套MVS系统，该系统利用非结构化的图片实现鲁棒且稠密的建模。本文的主要贡献是深度和法向量的联合估计，用光度和几何先验进行像素筛选，多视图几何一致项，该项同时进行精修和基于图片的深度和法向量的融合。在标准数据和大尺度网络图片上的实验证明了其在精度、完善性、效率方面的优异性能。

2. 引言

主要贡献

• Pixelwise normal estimation embedded into an improved PatchMatch sampling scheme.
• Pixelwise view selection using triangulation angle, incident angle, and image resolution-based geometric priors.
• Integration of a \temporal" view selection smoothness term.
• Adaptive window support through bilateral photometric consistency for improved occlusion boundary behavior.
• Introduction of a multi-view geometric consistency term for simultaneous depth/normal estimation and image-based fusion.
• Reliable depth/normal filtering and fusion.

2. 代码地址

github.com/colmap/colmap

参考文献

COLMAP： Pixelwise View Selection for Unstructured Multi-View Stereo - 知乎

Pixelwise View Selection for Unstructured Multi-View Stereo