Self-Supervised Learning of Monocular Depth Estimation Based on Progressive Strategy

Monocular depth estimation has been carried out with convolutional neural networks (CNNs). However, vast quantities of ground truth depth data are required as a supervising signal. Recently, self-supervised learning is explored for monocular depth estimation, which uses the minor loss of image reconstruction, rather than depth information, as the supervising signal in the training phase. However, the blurring problem often occurs on the surface of a near object when using the image reconstruction as a major loss function. Here, we propose a novel Progressive Strategy Network (PSNet) to overcome this problem, which optimizes the depth map from coarse to fine with several progressive modules. A progressive module estimates a coarse depth map with a color image by CNNs and outputs it into the next module for progressively accurate refinement. It can simplify the difficulty of estimating the depth map with a large size and reduce the blurring problem. Experiments demonstrate that the proposed approach consistently improves the quality of depth map on the surface of large objects and keeps edges clear. Our proposed method performs high-quality depth estimation on the KITTI dataset and achieves a strong generalization on the Make3D dataset. Besides, it can be flexibly used on the real scenes captured by mobile phones without extra training.