Song Wang, Jianke Zhu*, Ruixiang Zhang

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

	Prediction	Groud Truth
Perspective View
Bird's-Eye View

Please download the original SemanticKITTI dataset from the official website.

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!

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

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

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

@article{wang2022metarangeseg,
  title={Meta-RangeSeg: LiDAR Sequence Semantic Segmentation Using Multiple Feature Aggregation},
  author={Song Wang and Jianke Zhu and Ruixiang Zhang},
  journal={IEEE Robotics and Automation Letters},
  volume={7},
  number={4},
  pages={9739-9746},
  year={2022},
  publisher={IEEE}
}