  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. Code and more materials are available at
http://vis.xyz/pub/dtv-sfm.
