Self-supervised depth completion with attention-based loss

Deep completion which predicts dense depth from sparse depth has important applications in the fields of robotics, autonomous driving and virtual reality. It compensates for the shortcomings of low accuracy in monocular depth estimation. However, the previous deep completion works evenly processed each depth pixel and ignored the statistical properties of the depth value distribution. In this paper, we propose a self-supervised framework that can generate accurate dense depth from RGB images and sparse depth without the need for dense depth labels. We propose a novel attention-based loss that takes into account the statistical properties of the depth value distribution. We evaluate our approach on the KITTI Dataset. The experimental results show that our method achieves state-of-the-art performance. At the same time, ablation study proves that our method can effectively improve the accuracy of the results.