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BAAF-Net

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Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)

파일 탐색기

최종 버전 다운로드 (.zip)

- README.md
- README.md
- - compile_wrappers.sh
- - anno_paths.txt
  - class_names.txt
- - KDTreeTableAdaptor.h
  - knn.pyx
  - knn_.cxx
  - knn_.h
  - nanoflann.hpp
  - setup.py
  - test.py
- - compile_ops.sh
  - tf_sampling.cpp
  - tf_sampling.py
  - tf_sampling_g.cu
- 6_fold_cv.py
- data_prepare_s3dis.py
BAAFNet.py
compile_op.sh
cvpr2021_poster.pdf
helper_ply.py
helper_requirements.txt
helper_tf_util.py
helper_tool.py
kitti_08.png
LICENSE
main_S3DIS.py
README.md
s3dis.png
tester_S3DIS.py

# 설치 가이드

1. 코드 내려받기

git clone https://github.com/ShiQiu0419/BAAF-Net

깃허브에서 프로젝트 코드 전체를 내 컴퓨터로 내려받습니다.

cd BAAF-Net

방금 내려받은 프로젝트 폴더 안으로 이동합니다.

2. Python

쉬움 추천

사전 준비물

Git GitHub에서 프로젝트 코드를 내려받으려면 필요합니다.
Python 3 설치 시 'Add Python to PATH' 옵션을 꼭 체크하세요 (Windows).

⚠️ 이 프로젝트는 규모가 큰 저장소라, 이 방법이 실제 핵심 제품이 아니라 내부 하위 패키지를 가리키는 것일 수 있습니다. README 전체를 함께 확인해보세요.

pip install .

PyPI에 배포된 패키지를 바로 설치합니다. 소스 클론이 필요 없습니다.

python <실행할 파일명>.py # README에서 정확한 실행 파일명을 확인하세요

파이썬 스크립트(또는 모듈)를 실행합니다.

✅ 에러 메시지 없이 실행되고 터미널에 안내 문구가 출력되면 정상입니다.

// repository documentation

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# Semantic Segmentation for Real Point Cloud Scenes via Bilateral Augmentation and Adaptive Fusion (CVPR 2021)
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semantic-segmentation-for-real-point-cloud/semantic-segmentation-on-s3dis)](https://paperswithcode.com/sota/semantic-segmentation-on-s3dis?p=semantic-segmentation-for-real-point-cloud)  
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semantic-segmentation-for-real-point-cloud/semantic-segmentation-on-s3dis-area5)](https://paperswithcode.com/sota/semantic-segmentation-on-s3dis-area5?p=semantic-segmentation-for-real-point-cloud)  
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semantic-segmentation-for-real-point-cloud/semantic-segmentation-on-semantic3d)](https://paperswithcode.com/sota/semantic-segmentation-on-semantic3d?p=semantic-segmentation-for-real-point-cloud)  
[![PWC](https://img.shields.io/endpoint.svg?url=https://paperswithcode.com/badge/semantic-segmentation-for-real-point-cloud/3d-semantic-segmentation-on-semantickitti)](https://paperswithcode.com/sota/3d-semantic-segmentation-on-semantickitti?p=semantic-segmentation-for-real-point-cloud)

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](https://shiqiu0419.github.io/), [Saeed Anwar](https://saeed-anwar.github.io/), [Nick Barnes](http://users.cecs.anu.edu.au/~nmb/)  
IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR 2021)

## Paper and Citation
The paper can be downloaded from [here (CVF)](https://openaccess.thecvf.com/content/CVPR2021/papers/Qiu_Semantic_Segmentation_for_Real_Point_Cloud_Scenes_via_Bilateral_Augmentation_CVPR_2021_paper.pdf) or [here (arXiv)](https://arxiv.org/abs/2103.07074).  
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](https://drive.google.com/file/d/1GnHhfeItJDJCM0rIFLR5H7SrWRwO37Y4/view?usp=sharing) now. 
* **04/05/2021** Test results (sequence 11-21: mIoU: **59.9%**, OA: **89.8%**) for SemanticKITTI dataset are [available](https://drive.google.com/file/d/1FkyNfknwnJ2YnwUvPhMQGvJXCW--mqkK/view?usp=sharing) now.
* **04/05/2021** Validation results (sequence 08: mIoU: **58.7%**, OA: **91.3%**) for SemanticKITTI are [available](https://drive.google.com/file/d/1grQ57rZXL34mAOmI_3IASovu_APOPMI3/view?usp=sharing) now.
* **28/05/2021** Pretrained models can be downloaded on all 6 areas of S3DIS dataset are available at [google drive](https://drive.google.com/file/d/1DkZeMxJ_ibngwPiW5K0Celcx-nEbaBFb/view?usp=sharing). 
* **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 directory ```CUDA_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 as ```dataset_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 folder `original_data_dir` and the test results `base_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](https://drive.google.com/file/d/1DkZeMxJ_ibngwPiW5K0Celcx-nEbaBFb/view?usp=sharing).
* Download our results (**ply** files) via [google drive](https://drive.google.com/file/d/1GnHhfeItJDJCM0rIFLR5H7SrWRwO37Y4/view?usp=sharing) for visualizations/comparisons.
* More Functions about loading/writing/etc. **ply** files can be found from [here](https://github.com/ShiQiu0419/BAAF-Net/blob/main/helper_ply.py).

## 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](https://drive.google.com/file/d/1FkyNfknwnJ2YnwUvPhMQGvJXCW--mqkK/view?usp=sharing) for visualizations/comparisons.

<p align="center">
  <img width="1200" src="https://github.com/ShiQiu0419/BAAF-Net/blob/main/kitti_08.png">
</p>

* Validation results (sequence 08): mIoU: **58.7%**, OA: **91.3%**
* Download our **validation** results (sequence 08 **label** files) via [google drive](https://drive.google.com/file/d/1grQ57rZXL34mAOmI_3IASovu_APOPMI3/view?usp=sharing) for visualizations/comparisons.
* Visualization tools can be found from [semantic-kitti-api](https://github.com/PRBonn/semantic-kitti-api).

## Acknowledgment
The code is built on [RandLA-Net](https://github.com/QingyongHu/RandLA-Net). We thank the authors for sharing the codes.