単眼深度と自己運動推定のための自己教師ありスケール復元
Self-Supervised Scale Recovery for Monocular Depth and Egomotion Estimation
既知のカメラ高さと推定カメラ高さの一致を課す新たな損失で、単眼画像からメートルスケールの深度と自己運動を自己教師あり学習で推定する手法を提案した。
著者: Brandon Wagstaff, Jonathan Kelly
分類: cs.RO, cs.CV
原文アブストラクト
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.
