ldkong1205/LaserMix: [CVPR 2023 Highlight] LaserMix for Semi-Supervised...

LaserMix for Semi-Supervised LiDAR Semantic Segmentation

Lingdong Kong, Jiawei Ren, Liang Pan, Ziwei Liu
S-Lab, Nanyang Technological University

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.

Fig. Illustration for laser beam partition based on inclination φ.

Visit our project page to explore more details. 🚗

Updates

[2022.08] - LaserMix achieves 1st place among the semi-supervised semantic segmentation leaderboards of nuScenes, SemanticKITTI, and ScribbleKITTI, based on Paper-with-Code.
[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

Main Results

Framework Overview

Range View

Method	nuScenes				SemanticKITTI				ScribbleKITTI
Method	1%	10%	20%	50%	1%	10%	20%	50%	1%	10%	20%	50%
Sup.-only	38.3	57.5	62.7	67.6	36.2	52.2	55.9	57.2	33.1	47.7	49.9	52.5
LaserMix	49.5	68.2	70.6	73.0	43.4	58.8	59.4	61.4	38.3	54.4	55.6	58.7
improv. ↑	⁺11.2	⁺10.7	⁺7.9	⁺5.4	⁺7.2	⁺6.6	⁺3.5	⁺4.2	⁺5.2	⁺6.7	⁺5.7	⁺6.2

Voxel

Method	nuScenes				SemanticKITTI				ScribbleKITTI
Method	1%	10%	20%	50%	1%	10%	20%	50%	1%	10%	20%	50%
Sup.-only	50.9	65.9	66.6	71.2	45.4	56.1	57.8	58.7	39.2	48.0	52.1	53.8
LaserMix	55.3	69.9	71.8	73.2	50.6	60.0	61.9	62.3	44.2	53.7	55.1	56.8
improv. ↑	⁺4.4	⁺4.0	⁺5.2	⁺2.0	⁺5.2	⁺3.9	⁺4.1	⁺3.6	⁺5.0	⁺5.7	⁺3.0	⁺3.0

Ablation Studies

Qualitative Examples

qualitative

TODO List

[x] Initial release. :rocket:
[x] Add license. See here for more details.
[x] Add visualization video. :movie_camera:
[ ] Add demo at Hugging Face Spaces. :hugs:
[ ] 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

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

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

Citation

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

@ARTICLE{kong2022lasermix,
  title={LaserMix for Semi-Supervised LiDAR Semantic Segmentation},
  author={Kong, Lingdong and Ren, Jiawei and Pan, Liang and Liu, Ziwei},
  journal={arXiv preprint arXiv:2207.00026}, 
  year={2022},
}

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