Monocular depth estimators either require explicit scale supervision through auxiliary sensors or suffer from scale ambiguity, which renders them difficult to deploy in downstream applications. A possible source of scale is the sizes of objects found in the scene, but inaccurate localization makes them difficult to exploit. In this paper, we introduce a novel scale-aware monocular depth estimation method called StableCamH that does not require any auxiliary sensor or supervision. The key idea is to exploit prior knowledge of object heights in the scene but aggregate the height cues into a single invariant measure common to all frames in a road video sequence, namely the camera height. By formulating monocular depth estimation as camera height optimization, we achieve robust and accurate unsupervised end-to-end training. To realize StableCamH, we devise a novel learning-based size prior that can directly convert car appearance into its dimensions. Extensive experiments on KITTI and Cityscapes show the effectiveness of StableCamH, its state-of-the-art accuracy compared with related methods, and its generalizability. The training framework of StableCamH can be used for any monocular depth estimation method and will hopefully become a fundamental building block for further work.

本文介绍了一种名为StableCamH的新型尺度感知单目深度估计方法，它利用场景中物体的高度先验知识，将深度估计问题转化为相机高度优化，通过无监督端到端训练实现了稳健准确的估计，并且提出了一种可直接将车辆外观转化为尺寸的基于学习的尺寸先验方法。在KITTI和Cityscapes数据集上的大量实验证明了StableCamH的有效性、与相关方法相比的最先进准确性以及其通用性，该训练框架可用于任何单目深度估计方法，希望能够成为进一步研究的基础组成部分。

摄像机高度不变：无监督单目尺度感知道路场景深度估计