Self-Supervised Dense Depth Estimation with Panoramic Image and Sparse Lidar

Chenwei Lyu, Huai Yu, Zhipeng Zhao, Pengliang Ji, Xiangli Yang, Wen Yang

Abstract: The 360-depth estimation with spherical images and LiDAR data has recently become increasingly popular in autonomous driving and scene reconstruction. Compared with perspective images, spherical images have omnidirectional FoV, which exceedingly matches LiDAR data. However, the spherical distortion makes the 360-depth estimation a great challenge. To address this problem, we propose a self-supervised 360 depth estimation network in this paper. The network consists of a spherical convolution branch to extract panoramic image features and a ResNet branch to extract LiDAR features. Then an attention-based decoder is designed to estimate the depth. The reprojection error is used to self-supervise the network training. Experiments on the KITTI-360 dataset demonstrate the effectiveness of the proposed method.