Awesome
Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)
This repository is for BAAF-Net introduced in the following paper:
"Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion"
Shi Qiu, Saeed Anwar, Nick Barnes
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)
Paper and Citation
The paper can be downloaded from here (CVF) or here (arXiv).
If you find our paper/codes/results are useful, please cite:
@inproceedings{qiu2021semantic,
title={Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion},
author={Qiu, Shi and Anwar, Saeed and Barnes, Nick},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
pages={1757-1767},
year={2021}
}
Updates
- 04/05/2021 Results for S3DIS dataset (mIoU: 72.2%, OA: 88.9%, mAcc: 83.1%) are available now.
- 04/05/2021 Test results (sequence 11-21: mIoU: 59.9%, OA: 89.8%) for SemanticKITTI dataset are available now.
- 04/05/2021 Validation results (sequence 08: mIoU: 58.7%, OA: 91.3%) for SemanticKITTI are available now.
- 28/05/2021 Pretrained models can be downloaded on all 6 areas of S3DIS dataset are available at google drive.
- 28/05/2021 codes released!
Settings
- The project is tested on Python 3.6, Tensorflow 1.13.1 and cuda 10.0
- Then install the dependencies:
pip install -r helper_requirements.txt
- And compile the cuda-based operators:
sh compile_op.sh
(Note: may change the cuda root directoryCUDA_ROOT
in./util/sampling/compile_ops.sh
)
Dataset
- Download S3DIS dataset from <a href="https://docs.google.com/forms/d/e/1FAIpQLScDimvNMCGhy_rmBA2gHfDu3naktRm6A8BPwAWWDv-Uhm6Shw/viewform?c=0&w=1">here</a>.
- Unzip and move the folder
Stanford3dDataset_v1.2_Aligned_Version
to./data
. - Run:
python utils/data_prepare_s3dis.py
(Note: may specify other directory asdataset_path
in./util/data_prepare_s3dis.py
)
Training/Test
- Training:
python -B main_S3DIS.py --gpu 0 --mode train --test_area 5
(Note: specify the --test_area
from 1~6
)
- Test:
python -B main_S3DIS.py --gpu 0 --mode test --test_area 5 --model_path 'pretrained/Area5/snap-32251'
(Note: specify the --test_area
index and the trained model path --model_path
)
6-fold Cross Validation
- Conduct training and test on each area.
- Extract all test results,
Area_1_conferenceRoom_1.ply
...Area_6_pantry_1.ply
(272.ply
files in total), to the folder./data/results
- Run:
python utils/6_fold_cv.py
(Note: may change the target folderoriginal_data_dir
and the test resultsbase_dir
in./util/6_fold_cv.py
)
Pretrained Models and Results on S3DIS Dataset
<p align="center"> <img width="1000" src="https://github.com/ShiQiu0419/BAAF-Net/blob/main/s3dis.png"> </p>- BAAF-Net pretrained models on all 6 areas can be downloaded from google drive.
- Download our results (ply files) via google drive for visualizations/comparisons.
- More Functions about loading/writing/etc. ply files can be found from here.
Results on SemanticKITTI Dataset
- Online test results (sequence 11-21): mIoU: 59.9%, OA: 89.8%
- Download our test results (sequence 11-21 label files) via google drive for visualizations/comparisons.
- Validation results (sequence 08): mIoU: 58.7%, OA: 91.3%
- Download our validation results (sequence 08 label files) via google drive for visualizations/comparisons.
- Visualization tools can be found from semantic-kitti-api.
Acknowledgment
The code is built on RandLA-Net. We thank the authors for sharing the codes.