LaserMix

About

LaserMix is a semi-supervised learning (SSL) framework designed for LiDAR semantic segmentation. It leverages the strong spatial prior of driving scenes to construct low-variation areas via laser beam mixing, and encourages segmentation models to make confident and consistent predictions before and after mixing.

Visit our project page to explore more details. :red_car:

:books: Citation

If you find this work helpful, please kindly consider citing our papers:

@inproceedings{kong2023lasermix,
    title     = {{LaserMix} for Semi-Supervised {LiDAR} Semantic Segmentation},
    author    = {Kong, Lingdong and Ren, Jiawei and Pan, Liang and Liu, Ziwei},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    pages     = {21705-21715},
    year      = {2023}
}

@article{kong2025multi,
    title     = {Multi-Modal Data-Efficient {3D} Scene Understanding for Autonomous Driving},
    author    = {Kong, Lingdong and Xu, Xiang and Ren, Jiawei and others},
    journal   = {IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)},
    volume    = {47},
    number    = {5},
    pages     = {3748-3765},
    year      = {2025}
}

Updates

• [2024.05] - Our improved framework, LaserMix++ :rocket:, is avaliable on arXiv.
• [2024.01] - The toolkit tailored for The RoboDrive Challenge has been released. :hammer_and_wrench:
• [2023.12] - We are hosting The RoboDrive Challenge at ICRA 2024. :blue_car:
• [2023.12] - Introducing FRNet, an efficient and effective real-time LiDAR segmentation model that achieves promising semi-supervised learning results on SemanticKITTI and nuScenes. Code and checkpoints are available for downloading.
• [2023.03] - Intend to test the robustness of your LiDAR semantic segmentation models? Check our recent work, :robot: Robo3D, a comprehensive suite that enables OoD robustness evaluation of 3D segmentors on our newly established datasets: SemanticKITTI-C, nuScenes-C, and WOD-C.
• [2023.03] - LaserMix was selected as a :sparkles: highlight :sparkles: at CVPR 2023 (top 10% of accepted papers).
• [2023.02] - LaserMix was accepted to CVPR 2023! :tada:
• [2023.02] - LaserMix has been integrated into the MMDetection3D codebase! Check this PR in the dev-1.x branch to know more details. :beers:
• [2023.01] - As suggested, we will establish a sequential track taking into account the LiDAR data collection nature in our semi-supervised LiDAR semantic segmentation benchmark. The results will be gradually updated in RESULT.md.
• [2022.12] - We support a wider range of LiDAR segmentation backbones, including RangeNet++, SalsaNext, FIDNet, CENet, MinkowskiUNet, Cylinder3D, and SPVCNN, under both fully- and semi-supervised settings. The checkpoints will be available soon!
• [2022.12] - The derivation of spatial-prior-based SSL is available here. Take a look! :memo:
• [2022.08] - LaserMix achieves 1st place among the semi-supervised semantic segmentation leaderboards of nuScenes, SemanticKITTI, and ScribbleKITTI, based on Paper-with-Code. :bar_chart:
• [2022.08] - We provide a video demo for visual comparisons on the SemanticKITTI val set. Take a look!
• [2022.07] - Our paper is available on arXiv, click here to check it out. Code will be available soon!

Outline

• Installation
• Data Preparation
• Getting Started
• Video Demo
• Main Results
• TODO List
• License
• Acknowledgement
• Citation

Installation

Please refer to INSTALL.md for the installation details.

Data Preparation

Please refer to DATA_PREPARE.md for the details to prepare the 1nuScenes, 2SemanticKITTI, and 3ScribbleKITTI datasets.

Getting Started

Please refer to GET_STARTED.md to learn more usage about this codebase.

Video Demo

Demo 1	Demo 2	Demo 3
Link :arrow_heading_up:	Link :arrow_heading_up:	Link :arrow_heading_up:

Main Result

Framework Overview

Range View

Voxel

Ablation Studies

Qualitative Examples

Checkpoints & More Results

For more experimental results and pretrained weights, please refer to RESULT.md.

TODO List

• Initial release. :rocket:
• Add license. See here for more details.
• Add video demos :movie_camera:
• Add installation details.
• Add data preparation details.
• Add evaluation details.
• Add training details.

License

This work is under the Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.

Acknowledgement

This work is developed based on the MMDetection3D codebase.

MMDetection3D is an open-source toolbox based on PyTorch, towards the next-generation platform for general 3D perception. It is a part of the OpenMMLab project developed by MMLab.

We acknowledge the use of the following public resources during the course of this work: 1nuScenes, 2nuScenes-devkit, 3SemanticKITTI, 4SemanticKITTI-API, 5ScribbleKITTI, 6FIDNet, 7CENet, 8SPVNAS, 9Cylinder3D, 10TorchSemiSeg, 11MixUp, 12CutMix, 13CutMix-Seg, 14CBST, 15MeanTeacher, and 16Cityscapes.

We would like to thank Fangzhou Hong for the insightful discussions and feedback. ❤️