LiDAR Distillation

Paper | Model

LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection Yi Wei, Zibu Wei, Yongming Rao, Jiaxin Li, Jiwen Lu, Jie Zhou


In this paper, we propose the LiDAR Distillation to bridge the domain gap induced by different LiDAR beams for 3D object detection. In many real-world applications, the LiDAR points used by mass-produced robots and vehicles usually have fewer beams than that in large-scale public datasets. Moreover, as the LiDARs are upgraded to other product models with different beam amount, it becomes challenging to utilize the labeled data captured by previous versions’ high-resolution sensors. Despite the recent progress on domain adaptive 3D detection, most methods struggle to eliminate the beam-induced domain gap.

Model Zoo

Cross-dataset Adaptation

model method AP_BEV AP_3D
SECOND-IoU Direct transfer 32.91 17.24
SECOND-IoU ST3D 35.92 20.19
SECOND-IoU Ours 40.66 22.86
SECOND-IoU Ours (w / ST3D) 42.04 24.50
PV-RCNN Direct transfer 34.50 21.47
PV-RCNN ST3D 36.42 22.99
PV-RCNN Ours 43.31 25.63
PV-RCNN Ours (w / ST3D) 44.08 26.37
PointPillar Direct transfer 27.8 12.1
PointPillar ST3D 30.6 15.6
PointPillar Ours 40.23 19.12
PointPillar Ours (w / ST3D) 40.83 20.97

Results of cross-dataset adaptation from Waymo to nuScenes. The training Waymo data used in our work is version 1.0.

Single-dataset Adaptation

beams method AP_BEV AP_3D
32 Direct transfer 79.81 65.91
32 ST3D 71.29 57.57
32 Ours 82.22 70.15
32* Direct transfer 73.56 57.77
32* ST3D 67.08 53.30
32* Ours 79.47 66.96
16 Direct transfer 64.91 47.48
16 ST3D 57.58 42.40
16 Ours 74.32 59.87
16* Direct transfer 56.32 38.75
16* ST3D 55.63 37.02
16* Ours 70.43 55.24

Results of single-dataset adaptation on KITTI dataset with PointPillars (moderate difficulty). For SECOND-IoU and PV-RCNN, we find that it is easy to raise cuda error on low-beam data, which is may caused by the bug in spconv. Thus, we do not provide the model but you can still run these experiments with the yamls.


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Getting Started

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Our code is released under the Apache 2.0 license.


Our code is heavily based on OpenPCDet v0.2 and ST3D. Thanks OpenPCDet Development Team for their awesome codebase.


If you find this project useful in your research, please consider cite:

    title={LiDAR Distillation: Bridging the Beam-Induced Domain Gap for 3D Object Detection},
    author={Wei, Yi and Wei, Zibu and Rao, Yongming and Lu, Jiwen and Zhou, Jie},
    journal={arXiv preprint arXiv:2022.6980},

    title={OpenPCDet: An Open-source Toolbox for 3D Object Detection from Point Clouds},
    author={OpenPCDet Development Team},
    howpublished = {\url{}},


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