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.

• MVPNet

• 3DMV

• PointNet2

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}
}