%0 Generic %1 2020-wagstaff %A Wagstaff, Brandon %A Kelly, Jonathan %D 2020 %K ambiguity camera estimation height median monocular recovery scale %T Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation %X 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 have more information available. Further, we demonstrate how 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 that only recovers scale at test time.

@misc{2020-wagstaff, abstract = {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 have more information available. Further, we demonstrate how 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 that only recovers scale at test time.}, added-at = {2021-07-06T17:14:11.000+0200}, author = {Wagstaff, Brandon and Kelly, Jonathan}, biburl = {https://www.bibsonomy.org/bibtex/246db4837e07345ab4e09b3323b5deea2/pkoch}, interhash = {0ae7d97bd3cf0887a18945bc38a3c87e}, intrahash = {46db4837e07345ab4e09b3323b5deea2}, keywords = {ambiguity camera estimation height median monocular recovery scale}, note = {Manuscript submitted to the IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2021)}, timestamp = {2021-07-15T14:00:30.000+0200}, title = {Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation}, year = 2020 }