  In this paper, we propose a cross-modal distillation method named
StereoDistill to narrow the gap between the stereo and LiDAR-based approaches
via distilling the stereo detectors from the superior LiDAR model at the
response level, which is usually overlooked in 3D object detection
distillation. The key designs of StereoDistill are: the X-component Guided
Distillation~(XGD) for regression and the Cross-anchor Logit Distillation~(CLD)
for classification. In XGD, instead of empirically adopting a threshold to
select the high-quality teacher predictions as soft targets, we decompose the
predicted 3D box into sub-components and retain the corresponding part for
distillation if the teacher component pilot is consistent with ground truth to
largely boost the number of positive predictions and alleviate the mimicking
difficulty of the student model. For CLD, we aggregate the probability
distribution of all anchors at the same position to encourage the highest
probability anchor rather than individually distill the distribution at the
anchor level. Finally, our StereoDistill achieves state-of-the-art results for
stereo-based 3D detection on the KITTI test benchmark and extensive experiments
on KITTI and Argoverse Dataset validate the effectiveness.
