Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation (ICCV 2023)
Created by Lizhao Liu, Xunlong Xiao, Zhuangwei Zhuang from the South China University of Technology.
This repository contains the official PyTorch implementation of our ICCV 2023 paper Contextual Point Cloud Modeling for Weakly-supervised Point Cloud Semantic Segmentation.
Environment Setup
Our codebase is based on MinkowskiEngine, a high performance sparse convolution library built on PyTorch.
We recommend to use MinkowskiEngine 0.5.4, since it is much faster than 0.4.3
For MinkowskiEngine 0.5.4, see instruction in me054
For MinkowskiEngine 0.4.3, see instruction in me043
Data Preparation
We perform experiments on the following dataset
The preprocessed datasets are shared via google drive
Or see instruction in Dataset Preparation Hand-by-hand to prepare by yourself.
Quantitative Results
All results below are in mIoU(%)
Experiments on the indoor dataset: ScanNet V2 and S3DIS
| Method | ScanNet V2 | S3DIS | ||
| 0.01% | 0.1% | 0.01% | 0.1% | |
| MinkNet | 37.6 | 60.3 | 47.7 | 62.9 |
| Consis-based | 44.2 (+6.6) | 61.8 (+1.5) | 52.9 (+5.2) | 64.9 (+2.0) |
| CPCM (Ours) | 52.2 (+14.6) | 63.8 (+3.5) | 59.3 (+11.6) | 66.3 (+3.4) |
Experiments on the outdoor dataset SemanticKITTY (FoV)
| Method | SemanticKITTY | ||
| 1% | 0.1% | 0.01% | |
| MinkNet | 37.0 | 30.8 | 23.7 |
| Consis-based | 43.7 (+6.7) | 38.8 (+8.0) | 30.0 (+6.3) |
| CPCM (Ours) | 47.8 (+10.8) | 44.0 (+13.2) | 34.7 (+11.0) |
Qualitative Results for ScanNet and S3DIS
- The first two rows are results for ScanNet, the last two rows are results for S3DIS
Experiments on S3DIS
To reproduce the results of S3DIS, see experiment scripts here for details.
Experiments on ScanNet V2
To reproduce the results of ScanNet V2, see experiment scripts here for details. The script that generates ScanNet testset results are also available here.
Experiments on SemanticKITTY (FoV)
To reproduce the results of ScanNet V2, see experiment scripts here for details.
Acknowledgement
This codebase is partially built on the PointContrast project.
Citation
If you find this code helpful for your research, please consider citing
@inproceedings{liu2023contextual,
title={CPCM: Contextual point cloud modeling for weakly-supervised point cloud semantic segmentation},
author={Liu, Lizhao and Zhuang, Zhuangwei and Huang, Shangxin and Xiao, Xunlong and Xiang Tianhang and Chen, Cen and Wang, Jingdong and Tan, Mingkui},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2023}
}