Distortion-Aware Depth Self-Updating for Self-Supervised Fisheye Monocular Depth Estimation

Abstract

Self-supervised monocular depth estimation for fisheye cameras has attracted much attention in recent years due to their large view range. However, the performances of existing methods in this field are generally limited due to the inevitable severe distortions in fisheye images. To address this problem, we propose a distortion-aware depth self-updating network for self-supervised fisheye monocular depth estimation called DDS-Net. The proposed DDS-Net method employs a coarse-to-fine learning strategy, in which an explored fine depth predictor for predicting final depth is optimized with the predicted scene depths by a pretrained coarse depth predictor. The fine depth predictor contains a distortion-aware fisheye cost volume construction module and a depth self-updating module. The distortion-aware fisheye cost volume construction module is designed to construct a fisheye cost volume by learning the corresponding feature matching cost between continuous fisheye frames, which enables more accurate pixel-level depth cues to be captured under severe distortions. Based on the constructed cost volume and the initial depth estimated by the pretrained coarse depth predictor, the depth self-updating module is designed to self-update the depth map in an iterative manner. Extensive experimental results on 3 fisheye datasets demonstrate that the proposed method significantly outperforms 14 state-of-the-art methods for fisheye monocular depth estimation.