Direct methods have shown promise on visual odometry and SLAM, leading to
greater accuracy and robustness over feature-based methods. However, offline
3-d reconstruction from internet images has not yet benefited from a joint,
photometric optimization over dense geometry and camera parameters. Issues such
as the lack of brightness constancy, and the sheer volume of data, make this a
more challenging task. This work presents a framework for jointly optimizing
millions of scene points and hundreds of camera poses and intrinsics, using a
photometric cost that is invariant to local lighting changes. The improvement
in metric reconstruction accuracy that it confers over feature-based bundle
adjustment is demonstrated on the large-scale Tanks & Temples benchmark. We
further demonstrate qualitative reconstruction improvements on an internet
photo collection, with challenging diversity in lighting and camera intrinsics.

本文提出一个通过联合光度优化密集几何和摄像机参数，用于 3D 重建的框架，使其比特征点 bundle 调整更具指标重建精度，该框架可以应用于大规模的数据集，并且在面对不同的光照和相机内参时有更好的表现。