Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation
This work addresses the scale ambiguity issue in self-supervised monocular depth and egomotion estimation, which is crucial for applications like robotics and autonomous driving, but it is incremental as it builds on existing self-supervised frameworks.
The paper tackles the problem of unknown scale in self-supervised monocular depth and egomotion estimation by introducing a novel scale recovery loss that enforces consistency with a known camera height, producing metric predictions. The method is competitive with other techniques requiring more information and facilitates network retraining in new environments, with the egomotion network showing more accurate estimates than a test-time-only scale recovery method.
The self-supervised loss formulation for jointly training depth and egomotion neural networks with monocular images is well studied and has demonstrated state-of-the-art accuracy. One of the main limitations of this approach, however, is that the depth and egomotion estimates are only determined up to an unknown scale. In this paper, we present a novel scale recovery loss that enforces consistency between a known camera height and the estimated camera height, generating metric (scaled) depth and egomotion predictions. We show that our proposed method is competitive with other scale recovery techniques that require more information. Further, we demonstrate that our method facilitates network retraining within new environments, whereas other scale-resolving approaches are incapable of doing so. Notably, our egomotion network is able to produce more accurate estimates than a similar method which recovers scale at test time only.