songw-zju / Meta-RangeSeg

The official implementation of "Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation" (RA-L with IROS 2022)
MIT License
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Meta-RangeSeg

Song Wang, Jianke Zhu*, Ruixiang Zhang

This is the official implementation of Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation [Paper] [Video]. arXiv
PWC

Prediction Groud Truth
Perspective View z z
Bird's-Eye View z z

Demo

Model Zoo

Model Task mIoU(paper)
[on test set]
mIoU(reprod.)
[on test set]
Results
Meta-RangeSeg multiple scans semantic segmentation 49.5 49.7 valid_pred
test_pred
Meta-RangeSeg single scan semantic segmentation 61.0 60.3 valid_pred
test_pred

Data Preparation

SemanticKITTI download

Please download the original SemanticKITTI dataset from the official website.

Residual image generation

For residual image generation, we provide an online version but adopt the offline one in the actual training. Please refer to LiDAR-MOS for more details. Thanks for their great work!

Testing Pretrained Models

You can run the following command to test the performance of Meta-RangeSeg:

cd ./train/tasks/semantic
python infer.py -d ./data/semantic_kitti/dataset -m ../../../logs

Training

To train the model from scratch, you can run:

CUDA_VISIBLE_DEVICES=0,1 python train.py -d ./data/semantic_kitti/dataset -ac ../../../meta_rangeseg.yml

Acknowledgment

This project is heavily based on SalsaNext and LiDAR-MOS. RangeDet and FIDNet are also excellent range-based models, which help us a lot.

Citations

@article{wang2022meta,
  title={Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation},
  author={Wang, Song and Zhu, Jianke and Zhang, Ruixiang},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={4},
  pages={9739--9746},
  year={2022},
  publisher={IEEE}
}