RoMo: Robust Motion Segmentation Improves Structure from Motion

There has been extensive progress in the reconstruction and generation of 4D scenes from monocular casuallycaptured video. Estimating accurate camera poses from videos through structure-from-motion (SfM) relies on robustly separating static and dynamic parts of a video. 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-theart on camera calibration for scenes with dynamic content, outperforming existing methods by a substantial margin.