Estimating depth from a single image represents an attractive alternative to
more traditional approaches leveraging multiple cameras. In this field, deep
learning yielded outstanding results at the cost of needing large amounts of
data labeled with precise depth measurements for training. An issue softened by
self-supervised approaches leveraging monocular sequences or stereo pairs in
place of expensive ground truth depth annotations. This paper enables to
further improve monocular depth estimation by integrating into existing
self-supervised networks a geometrical prior. Specifically, we propose a
sparsity-invariant autoencoder able to process the output of conventional
visual odometry algorithms working in synergy with depth-from-mono networks.
Experimental results on the KITTI dataset show that by exploiting the
geometrical prior, our proposal: i) outperforms existing approaches in the
literature and ii) couples well with both compact and complex depth-from-mono
architectures, allowing for its deployment on high-end GPUs as well as on
embedded devices (e.g., NVIDIA Jetson TX2).

该论文提出了一种自监督学习的方法，利用几何先验和自编码器来提高单目深度估计的效果，实验结果表明该方法在 KITTI 数据集上的表现优于现有方法，适用于高性能 GPU 和嵌入式设备。