https://github.com/QingyongHu/SQN
SQN in Tensorflow (ECCV'2022)
https://github.com/QingyongHu/SQN
annotations computer-vision deep-learning point-cloud weakly-supervised-segmentation
Last synced: about 1 month ago
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SQN in Tensorflow (ECCV'2022)
- Host: GitHub
- URL: https://github.com/QingyongHu/SQN
- Owner: QingyongHu
- License: mit
- Created: 2021-04-09T12:43:24.000Z (about 4 years ago)
- Default Branch: main
- Last Pushed: 2023-04-27T09:44:03.000Z (almost 2 years ago)
- Last Synced: 2025-03-03T23:02:53.300Z (about 2 months ago)
- Topics: annotations, computer-vision, deep-learning, point-cloud, weakly-supervised-segmentation
- Language: Python
- Homepage:
- Size: 12.7 MB
- Stars: 106
- Watchers: 13
- Forks: 10
- Open Issues: 20
-
Metadata Files:
- Readme: README.md
- License: LICENSE
Awesome Lists containing this project
README
[](https://arxiv.org/abs/2104.04891)
[](https://github.com/QingyongHu/SQN)
[](https://creativecommons.org/licenses/by-nc-sa/4.0/legalcode)
# SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds (ECCV2022)
This is the official repository of the **Semantic Query Network (SQN)**. For technical details, please refer to:
**SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds**
[Qingyong Hu](https://qingyonghu.github.io/), [Bo Yang](https://yang7879.github.io/), [Guangchi Fang]()
, [Ales Leonardis](https://www.cs.bham.ac.uk/~leonarda/),
[Yulan Guo](http://yulanguo.me/), [Niki Trigoni](https://www.cs.ox.ac.uk/people/niki.trigoni/)
, [Andrew Markham](https://www.cs.ox.ac.uk/people/andrew.markham/).
**[[Paper](https://arxiv.org/abs/2104.04891)] [[Video](https://youtu.be/Q6wICSRRw3s)]**
### (1) Setup
This code has been tested with Python 3.5, Tensorflow 1.11, CUDA 9.0 and cuDNN 7.4.1 on Ubuntu 16.04/Ubuntu 18.04.
- Clone the repository
```
git clone --depth=1 https://github.com/QingyongHu/SQN && cd SQN
```
- Setup python environment
```
conda create -n sqn python=3.5
source activate sqn
pip install -r helper_requirements.txt
sh compile_op.sh
```
### (2) Training (Semantic3D as example)
First, follow the RandLA-Net [instruction](https://github.com/QingyongHu/RandLA-Net) to prepare the dataset, and then
manually change the
dataset [path](https://github.com/QingyongHu/SQN/blob/f75eb51532a5319c0da5320c20f58fbe5cb3bbcd/main_Semantic3D.py#L17) here.
- Start training with weakly supervised setting:
```
python main_Semantic3D.py --mode train --gpu 0 --labeled_point 0.1%
```
- Evaluation:
```
python main_Semantic3D.py --mode test --gpu 0 --labeled_point 0.1%
```
Quantitative results achieved by our SQN:
|  |  |
| ------------------------------ | ---------------------------- |
|  |  |
### (3) Sparse Annotation Demo
### Citation
If you find our work useful in your research, please consider citing:
@inproceedings{hu2021sqn,
title={SQN: Weakly-Supervised Semantic Segmentation of Large-Scale 3D Point Clouds},
author={Hu, Qingyong and Yang, Bo and Fang, Guangchi and Guo, Yulan and Leonardis, Ales and Trigoni, Niki and Markham, Andrew},
booktitle={European Conference on Computer Vision},
year={2022}
}
## Related Repos
1. [RandLA-Net: Efficient Semantic Segmentation of Large-Scale Point Clouds](https://github.com/QingyongHu/RandLA-Net) 
2. [SoTA-Point-Cloud: Deep Learning for 3D Point Clouds: A Survey](https://github.com/QingyongHu/SoTA-Point-Cloud) 
3. [3D-BoNet: Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds](https://github.com/Yang7879/3D-BoNet) 
4. [SpinNet: Learning a General Surface Descriptor for 3D Point Cloud Registration](https://github.com/QingyongHu/SpinNet) 
5. [SensatUrban: Learning Semantics from Urban-Scale Photogrammetric Point Clouds](https://github.com/QingyongHu/SensatUrban) 
6. [Not All Points Are Equal: Learning Highly Efficient Point-based Detectors for 3D LiDAR Point Clouds](https://github.com/yifanzhang713/IA-SSD) 