KPRNet: Improving projection-based LiDARsemantic segmentation

Installation

Install apex and the packages in requirements.txt

Experiment

Download pre-trained resnext_cityscapes_2p.pth. The path should be given in model_dir. CityScapes pretraining will be added later.

The result from paper is trained on 8 16GB GPUs (total batch size 24).

To train run:

python train_kitti.py \
  --semantic-kitti-dir path_to_semantic_kitti \
  --model-dir location_where_your_pretrained_model_is \
  --checkpoint-dir your_output_dir

The fully trained model weights can be downloaded here .

Acknowledgments

KPConv

RangeNet++

HRNet

Reference

KPRNet appears in ECCV workshop Perception for Autonomous Driving.

@article{kochanov2020kprnet,
  title={KPRNet: Improving projection-based LiDAR semantic segmentation},
  author={Kochanov, Deyvid and Nejadasl, Fatemeh Karimi and Booij, Olaf},
  journal={arXiv preprint arXiv:2007.12668},
  year={2020}
}