LinK: Linear Kernel for LiDAR-based 3D Perception

Official PyTorch implementation of LinK, from the following paper:

LinK: Linear Kernel for LiDAR-based 3D Perception. CVPR 2023.
Tao Lu, Xiang Ding, Haisong Liu, Gangshan Wu, Limin Wang
Multimedia Computing Group, Nanjing University
[arxiv][Conference version]

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LinK is a large kernel backbone for 3D perception tasks, consisting of a linear kernel generator and a pre-aggregation strategy. The two designs scale up the perception range into 21x21x21 with linear complexity.

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Model Zoo

Segmentation on SemanticKITTI(val)

name	kernel config	mIoU	model
LinK	cos_x:(2x3)^3	67.72	model
LinK	cos:(3x7)^3	67.50	model
LinK(encoder-only)	cos_x:(2x3)^3	67.33	model
LinK(encoder-only)	cos:(3x5)^3	67.07	model

Detection on nuScenes

• Validation

name	kernel config	NDS	mAP	model
LinK	cos:(3x7)^3	69.5	63.6	model

• Test

name	kernel config	NDS	mAP	model
LinK	cos:(3x7)^3	71.0	66.3	model
LinK(TTA)	cos:(3x7)^3	73.4	69.8	model

Installation

Clone this repo to your workspace.

git clone https://github.com/MCG-NJU/LinK.git
cd LinK

Semantic Segmentation

please check segmentation/INSTALL.md and segmentation/GET_STARTED.md.

Detection

see detection/INSTALL.md and detection/GET_STARTED.md.

Citation

If you find our work helpful, please consider citing:

@InProceedings{lu2023link,
    author    = {Lu, Tao and Ding, Xiang and Liu, Haisong and Wu, Gangshan and Wang, Limin},
    title     = {LinK: Linear Kernel for LiDAR-Based 3D Perception},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2023},
    pages     = {1105-1115}
}

@article{lu2022app,
  title={APP-Net: Auxiliary-point-based Push and Pull Operations for Efficient Point Cloud Classification},
  author={Lu, Tao and Liu, Chunxu and Chen, Youxin and Wu, Gangshan and Wang, Limin},
  journal={arXiv preprint arXiv:2205.00847},
  year={2022}
}

Contact

• Tao Lu: [email protected]
• Xiang Ding: [email protected]
• Haisong Liu: [email protected]

Acknowledgement

Our code is based on CenterPoint, SPVNAS, spconv, torchsparse. And we thank a lot for the kind help from Ruixiang Zhang, Xu Yan and Yukang Chen.