by Hengshuang Zhao*, Li Jiang*, Chi-Wing Fu, and Jiaya Jia, details are in paper.
This repository is build for PointWeb in point cloud scene understanding.
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Requirement:
- Hardware: 4 GPUs (better with >=11G GPU memory)
- Software: PyTorch>=1.0.0, Python3, CUDA>=9.0, tensorboardX
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Clone the repository and build the ops:
git clone https://github.com/hszhao/PointWeb.git cd PointWeb cd lib/pointops && python setup.py install && cd ../../
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Train:
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Download related datasets and symlink the paths to them as follows (you can alternatively modify the relevant paths specified in folder
config):mkdir -p dataset ln -s /path_to_s3dis_dataset dataset/s3dis -
Specify the gpu used in config and then do training:
sh tool/train.sh s3dis pointweb
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Test:
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Download trained segmentation models and put them under folder specified in config or modify the specified paths.
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For full testing (get listed performance):
sh tool/test.sh s3dis pointweb
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Visualization: tensorboardX incorporated for better visualization.
tensorboard --logdir=run1:$EXP1,run2:$EXP2 --port=6789
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Other:
Description: mIoU/mAcc/aAcc/voxAcc stands for mean IoU, mean accuracy of each class, all pixel accuracy , and voxel label accuracy respectively.
mIoU/mAcc/aAcc of PointWeb on S3DIS dataset: 0.6055/0.6682/0.8658.
mIoU/mAcc/aAcc/voxAcc of PointWeb on ScanNet dataset: 0.5063/0.6061/0.8529/0.8568.
If you find the code or trained models useful, please consider citing:
@inproceedings{zhao2019pointweb,
title={{PointWeb}: Enhancing Local Neighborhood Features for Point Cloud Processing},
author={Zhao, Hengshuang and Jiang, Li and Fu, Chi-Wing and Jia, Jiaya},
booktitle={CVPR},
year={2019}
}