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Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss (CVPR 2023)

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ST-SLidR

ST-SLidR is a 2D to 3D distillation framework for autonomous driving scenes. This repository is based off of [SLidR].

Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss
Anas Mahmoud, Jordan Hu, Tianshu Kuai, Ali Harakeh, Liam Paull, Steven Waslander
[Paper]

Overview of the method

Installation

Please refer to INSTALL.md for the installation of ST-SLidR.

Results

All results are obtained by pre-training on nuScenes dataset. Each pretraining experiment is conducted 3 times, and the average performance is reported on Linear Probing using 100% of the labelled point cloud data and on finetuning using 1% of the labelled point cloud data.

Method Self-supervised
Encoder
Linear Probing
(100%)
Finetuning
(1%)
SLidR MoCoV2 38.08 40.01
ST-SLidR MoCoV2 40.56 41.13
SLidR SwAV 38.96 40.01
ST-SLidR SwAV 40.36 41.20
SLidR DINO 38.29 39.86
ST-SLidR DINO 40.36 41.07
SLidR oBoW 37.41 39.51
ST-SLidR oBoW 40.00 40.87

Citation

If you use ST-SLidR in your research, please consider citing:

@inproceedings{ST-SLidR,
   title = {Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss},
   author = {Anas Mahmoud and Jordan Hu and Tianshu Kuai and Ali Harakeh and  Liam Paull and  Steven Waslander},
   journal = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
   year = {2023}
}

License

ST-SLidR is released under the Apache 2.0 license.

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Self-Supervised Image-to-Point Distillation via Semantically Tolerant Contrastive Loss (CVPR 2023)

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