Uncertainty-Driven Dense Two-View Structure from Motion

TL;DR

This paper presents DTV-SfM, an uncertainty-aware dense two-view SfM pipeline that improves pose and depth estimation by leveraging per-pixel optical flow confidence and iterative refinement, achieving state-of-the-art results.

Contribution

It introduces an uncertainty-driven dense SfM framework combining confidence-aware optical flow, weighted bundle adjustment, and pose-depth consistency for enhanced accuracy.

Findings

Achieves superior depth accuracy on benchmark datasets.

Outperforms existing methods like SuperPoint and SuperGlue in pose estimation.

Demonstrates robustness through online iterative refinement.

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