Abstract

This work introduces an effective and practical solution to the dense two-view structure from motion (SfM) problem. One vital question addressed is how to mindfully use per-pixel optical flow correspondence between two frames for accurate pose estimation – as perfect per-pixel correspondence between two images is difficult, if not impossible, to establish. With the carefully estimated camera pose and predicted per-pixel optical flow correspondences, a dense depth of the scene is computed. Later, an iterative refinement procedure is introduced to further improve optical flow matching confidence, camera pose, and depth, exploiting their inherent dependency in rigid SfM. The fundamental idea presented is to benefit from per-pixel uncertainty in the optical flow estimation and provide robustness to the dense SfM system via an online refinement. Concretely, we introduce our uncertainty-driven Dense Two-View SfM pipeline (DTV-SfM), consisting of an uncertainty-aware dense optical flow estimation approach that provides per-pixel correspondence with their confidence score of matching; a weighted dense bundle adjustment formulation that depends on optical flow uncertainty and bidirectional optical flow consistency to refine both pose and depth; a depth estimation network that considers its consistency with the estimated poses and optical flow respecting epipolar constraint. Extensive experiments show that the proposed approach achieves remarkable depth accuracy and state-of-the-art camera pose results superseding SuperPoint and SuperGlue accuracy when tested on benchmark datasets such as DeMoN, YFCC100M, and ScanNet.

Result Visualization

Paper

Weirong Chen, Suryansh Kumar, Fisher Yu Uncertainty-Driven Dense Two-View Structure from Motion RA-L 2023

Code

github.com/SysCV/dtv-sfm

Citation

@article{chen2023uncertainty,
  title={Uncertainty-Driven Dense Two-View Structure from Motion},
  author={Chen, Weirong and Kumar, Suryansh and Yu, Fisher},
  journal={IEEE Robotics and Automation Letters},
  volume={8},
  number={3},
  pages={1763--1770},
  year={2023},
  publisher={IEEE}
}