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SAFNet

Implementation of our IROS 2021 paper: Similarity-Aware Fusion Network for 3D Semantic Segmentation

To get a rapid understanding of our paper and codes, you can also read this blog written by an anonymous reader in Chinese.

Environment Preparation & Data Preparation

We prepared our environment and ScanNet data as follows:

Environment:

  • Python 3.6
  • Pytorch 1.2.0
  • CUDA 10.0 & CUDNN 7.6.4

DATA:

  • The data is released under the ScanNet Term of Use, please contact ScanNet team for access.
  • See MVPNet repo for processing the raw data and resizing images.

Currently, the code is not clean.

The code is coming soon.

Pre-trained Model

We provide a pre-trained model (backbone: PointNet++ & ResNet34) which achieves 68.54% mIoU and 88.07% Accuracy on the validation set of ScanNetv2.

The validation log was written in this file.

Please check the BaiduDisk with the code [f4n6].

Core code

To see the corest part of our method, you can directly check this file.

Acknowledgements

We thank the authors of following works for opening source their excellent codes.

Citation

If you find our work useful, please cite our paper:

@inproceedings{zhao2021similarity,
  title={Similarity-Aware Fusion Network for 3D Semantic Segmentation},
  author={Zhao, Linqing and Lu, Jiwen and Zhou, Jie},
  booktitle={2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)},
  pages={1585--1592},
  year={2021},
  organization={IEEE}
}

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[IROS 2021] Implementation of "Similarity-Aware Fusion Network for 3D Semantic Segmentation"

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