Multi-body Depth and Camera Pose Estimation from Multiple Views.

Traditional and deep Structure-from-Motion (SfM) methods typically operate under the assumption that the scene is rigid, i.e., the environment is static or consists of a single moving object. Few multi-body SfM approaches address the reconstruction of multiple rigid bodies in a scene but suffer from the inherent scale ambiguity of SfM, such that objects are reconstructed at inconsistent scales. We propose a depth and camera pose estimation framework to resolve the scale ambiguity in multi-body scenes. Specifically, starting from disorganized images, we present a novel multi-view scale estimator that resolves the camera pose ambiguity and a multi-body plane sweep network that generalizes depth estimation to dynamic scenes. Experiments demonstrate the advantages of our method over state-of-the-art SfM frameworks in multi-body scenes and show that it achieves comparable results in static scenes. The code and dataset are available at https://github.com/andreadalcin/MultiBodySfM.