  Stereo-based 3D detection aims at detecting 3D object bounding boxes from
stereo images using intermediate depth maps or implicit 3D geometry
representations, 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
models can encode accurate object boundaries and surface normal directions from
LiDAR point clouds. However, the detection results of stereo-based detectors
are easily affected by the erroneous depth features 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.
