LIGA-Stereo: Learning LiDAR Geometry Aware Representations for Stereo-based 3D Detector

Stereo-based 3D detection aims at detecting 3D objects from stereo images, which provides a low-cost solution for 3D perception. However, its performance is still inferior compared with LiDAR-based detection algorithms. To detect and localize accurate 3D bounding boxes, LiDAR-based detectors encode high-level representations from Li-DAR point clouds, such as accurate object boundaries and surface normal directions. In contrast, high-level features learned by stereo-based detectors are easily affected by the erroneous depth estimation due to the limitation of stereo matching. To solve the problem, we propose LIGA-Stereo (LiDAR Geometry Aware Stereo Detector) to learn stereo-based 3D detectors under the guidance of high-level geometry-aware representations of LiDAR-based detection models. In addition, we found existing voxel-based stereo detectors failed to learn semantic features effectively from indirect 3D supervisions. We attach an auxiliary 2D detection head to provide direct 2D semantic supervisions. Experiment results show that the above two strategies improved the geometric and semantic representation capabilities. Compared with the state-of-the-art stereo detector, our method has improved the 3D detection performance of cars, pedestrians, cyclists by 10.44%, 5.69%, 5.97% mAP respectively on the official KITTI benchmark. The gap between stereo-based and LiDAR-based 3D detectors is further narrowed. The code is available at https://xy-guo.github.io/liga/.

1. Introduction

In recent years, LiDAR-based 3D detection [57], [28], [46], [45], [61], [60] has achieved increasing performance and stability in autonomous driving and robotics. However, the high cost of LiDAR sensors has limited its applications in low-cost products. Stereo matching [3], [18], [26], [35] is the most common depth sensing technique using only cameras. Compared with LiDAR sensors, stereo cameras are at a much lower cost and higher resolutions, which makes it a suitable alternative solution for 3D perception. 3D detection from stereo images aims at detecting objects using estimated depth maps [39], [52], [64] or implicit 3D geometry representations [30], [10], [53]. However, the performance of existing stereo-based 3D detection algorithms is still inferior compared with LiDAR-based algorithms.