Awesome
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
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}
}