• RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
• • Introduction
  • Citation
  • Environment Dependencies
  • Datasets
  • Training & Testing
  • • RDFC-GAN Training
    • RDFC-GAN Testing
    • RDF-GAN Testing
  • Dependencies
  • License

RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion

This repository provides the code for our paper, RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion in TPAMI and its previous version RGB-Depth Fusion GAN for Indoor Depth Completion (RDF-GAN) in CVPR 2022.

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RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion
[arXiv] [IEEE DL]

IEEE Transactions on Pattern Analysis and Machine Intelligence 2024

Authors: Haowen Wang*, Zhengping Che*, Yufan Yang, Mingyuan Wang, Zhiyuan Xu, Xiuquan Qiao, Mengshi Qi, Feifei Feng, and Jian Tang (*equal contributions)

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RGB-Depth Fusion GAN for Indoor Depth Completion
[arXiv]

Proceedings of the 35th IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2022

Authors: Haowen Wang, Mingyuan Wang, Zhengping Che, Zhiyuan Xu, Xiuquan Qiao, Mengshi Qi, Feifei Feng, and Jian Tang

Introduction

We design a two-branch end-to-end GAN-based fusion network, which takes a pair of RGB and incomplete depth images as input to predict a dense and completed depth map.

Citation

If you find our work helpful in your research, please consider citing:

• RDFC-GAN

@ARTICLE{refcgan10497905,
  author={Wang, Haowen and Che, Zhengping and Yang, Yufan and Wang, Mingyuan and Xu, Zhiyuan and Qiao, Xiuquan and Qi, Mengshi and Feng, Feifei and Tang, Jian},
  journal={IEEE Transactions on Pattern Analysis and Machine Intelligence}, 
  title={RDFC-GAN: RGB-Depth Fusion CycleGAN for Indoor Depth Completion}, 
  year={2024},
  volume={},
  number={},
  pages={1-14},
  keywords={Depth completion;Generative adversarial network;RGB-depth fusion;Indoor environment},
  doi={10.1109/TPAMI.2024.3388004}}

• RDF-GAN

@inproceedings{wang2022rgb,
  title={Rgb-depth fusion gan for indoor depth completion},
  author={Wang, Haowen and Wang, Mingyuan and Che, Zhengping and Xu, Zhiyuan and Qiao, Xiuquan and Qi, Mengshi and Feng, Feifei and Tang, Jian},
  booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
  pages={6209--6218},
  year={2022}
}

Environment Dependencies

We recommend using a conda environment:

conda create -n RDFC_GAN python=3.8  
conda activate RDFC_GAN

# pytorch installation
conda install pytorch==1.6.0 torchvision==0.7.0 cudatoolkit=10.2 -c pytorch

# DCN installation
cd lib/models/generator/rdf_generator/nlspn/deformconv
python setup.py build install
cd PATH_TO_RDF-GAN-V2

# other dependencies
cd ..
pip install -r requirements.txt

Datasets

NYUv2 (official): We used preprocessed NYUv2 HDF5 dataset provided by Fangchang Ma.

SUN RGB-D (official): For the overall prepare process, please refer to the README page for SUN RGB-D.

Training & Testing

You can adjust your own configuration in config.py, such as batch size, work directory, semantic label setting, etc. Alternatively, you can directly change these settings from the command line or shell script.

RDFC-GAN Training

To train on the NYUv2 dataset with default settings:

bash train.sh

RDFC-GAN Testing

To test on the NYUv2 dataset with default settings:

bash test.sh

RDF-GAN Testing

bash RDF-GAN/bash/'***'.sh --load_from 'your chekpoint'

Dependencies

This project uses the following third-party code:

• Surface Normal Estimation (SNE): Used for surface normal estimation. This code is licensed under the MIT License.

License

This project is licensed under the Apache License 2.0.