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.

本文提出一种新的尺度恢复 loss 方法，通过强制实现已知摄像机高度和估计出的摄像机高度之间的一致性，以生成度量（缩放）深度和自运动预测。结果表明，我们提出的方法在网络重新训练方面具有其他尺度恢复技术无法实现的优势，并且我们的自运动网络能够比仅在测试时恢复尺度的类似方法产生更准确的估计结果。