CrossNet

KO | EN

gitlite — search

#python #react #ai #typescript #go #machine-learning #nodejs #macos #java #windows #cli #security #ios #openai

(★ 11)

[TMM 2023] Self-Supervised Intra-Modal and Cross-Modal Contrastive Learning for Point Cloud Understanding

CrossNet 최신버젼 다운로드

최종 버전 다운로드 (.zip)

// repository documentation

Was this content helpful?

★ 0 (0 ratings)

# [TMM 2023] Self-Supervised Intra-Modal and Cross-Modal Contrastive Learning for Point Cloud Understanding

#### [Paper Link](https://ieeexplore.ieee.org/abstract/document/10147273/) | [Project Page](https://github.com/liujia99/CrossNet/)

## Introduction

This paper proposes a self-supervised point cloud understanding method called CrossNet. CrossNet is simple and efficient, developing the intra-modal contrastive loss between the point clouds and the cross-modal contrastive loss between the point clouds and images. Finally, we combine the overall training objectives.

## Citation

If you entrust our work with value, please consider giving a star ⭐ and citation.

```bibtex
@article{wu2023self,
  title={Self-Supervised Intra-Modal and Cross-Modal Contrastive Learning for Point Cloud Understanding},
  author={Wu, Yue and Liu, Jiaming and Gong, Maoguo and Gong, Peiran and Fan, Xiaolong and Qin, AK and Miao, Qiguang and Ma, Wenping},
  journal={IEEE Transactions on Multimedia},
  year={2023},
  publisher={IEEE}
}
```

## Dependencies

Refer `requirements.txt` for the required packages.

## Download data

Datasets are available [here](https://drive.google.com/drive/folders/1dAH9R3XDV0z69Bz6lBaftmJJyuckbPmR?usp=sharing). Run the command below to download all the datasets (ShapeNetRender, ModelNet40, ScanObjectNN, ShapeNetPart) to reproduce the results. Additional [S3DIS](http://buildingparser.stanford.edu/dataset.html) is optional.

```
cd data
source download_data.sh
```

## Train CrossNet

Refer `python train_crossnet_con.py` for the command to train CrossNet.

## Downstream Tasks

### 1. 3D Object Classification

Run `downstream/classification/main.py`  to perform linear SVM object classification in both ModelNet40 and ScanObjectNN datasets.

### 2. 3D Object Part Segmentation

Refer `downstream/segmentation/main_partseg.py` for fine-tuning experiment for part segmentation in ShapeNetPart dataset.

### 3. 3D Object Semantic Segmentation

Refer `downstream/segmentation/main_semseg.py` for fine-tuning experiment for semantic segmentation in S3DIS dataset.

## Acknowledgements

Our code borrows heavily from [CrossPoint](https://github.com/MohamedAfham/CrossPoint) repository. We thank the authors of CrossPoint for releasing their code.

CrossNet

CrossNet 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

CrossNet

CrossNet 최신버젼 다운로드

오늘의 추천 프로젝트

Was this content helpful?

Recent Feedback

Download README