Advancing Self-supervised Monocular Depth Learning with Sparse LiDAR
This work addresses accuracy issues in depth prediction for autonomous robots, offering a real-time solution that is incremental by building on existing sparse LiDAR methods.
The paper tackles the problem of low accuracy in self-supervised monocular depth prediction by proposing FusionDepth, a two-stage network that fuses monocular image features with sparse LiDAR features to predict and refine depth maps. It significantly outperforms state-of-the-art methods, achieving over 68% improvement in monocular 3D object detection on the KITTI Leaderboard.
Self-supervised monocular depth prediction provides a cost-effective solution to obtain the 3D location of each pixel. However, the existing approaches usually lead to unsatisfactory accuracy, which is critical for autonomous robots. In this paper, we propose FusionDepth, a novel two-stage network to advance the self-supervised monocular dense depth learning by leveraging low-cost sparse (e.g. 4-beam) LiDAR. Unlike the existing methods that use sparse LiDAR mainly in a manner of time-consuming iterative post-processing, our model fuses monocular image features and sparse LiDAR features to predict initial depth maps. Then, an efficient feed-forward refine network is further designed to correct the errors in these initial depth maps in pseudo-3D space with real-time performance. Extensive experiments show that our proposed model significantly outperforms all the state-of-the-art self-supervised methods, as well as the sparse-LiDAR-based methods on both self-supervised monocular depth prediction and completion tasks. With the accurate dense depth prediction, our model outperforms the state-of-the-art sparse-LiDAR-based method (Pseudo-LiDAR++) by more than 68% for the downstream task monocular 3D object detection on the KITTI Leaderboard. Code is available at https://github.com/AutoAILab/FusionDepth