There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casually-captured video. While these tasks rely heavily on known camera poses, the problem of finding such poses using structure-from-motion (SfM) often depends on robustly separating static from dynamic parts of a video. The lack of a robust solution to this problem limits the performance of SfM camera-calibration pipelines. We propose a novel approach to video-based motion segmentation to identify the components of a scene that are moving w.r.t. a fixed world frame. Our simple but effective iterative method, RoMo, combines optical flow and epipolar cues with a pre-trained video segmentation model. It outperforms unsupervised baselines for motion segmentation as well as supervised baselines trained from synthetic data. More importantly, the combination of an off-the-shelf SfM pipeline with our segmentation masks establishes a new state-of-the-art on camera calibration for scenes with dynamic content, outperforming existing methods by a substantial margin.
@article{arxiv.2411.18650,
title = {RoMo: Robust Motion Segmentation Improves Structure from Motion},
author = {Lily Goli and Sara Sabour and Mark Matthews and Marcus Brubaker and Dmitry Lagun and Alec Jacobson and David J. Fleet and Saurabh Saxena and Andrea Tagliasacchi},
journal= {arXiv preprint arXiv:2411.18650},
year = {2024}
}