RGB-D Salient Object Detection via 3D Convolutional Neural Networks (AAAI 2021)

3-D Convolutional Neural Networks for RGB-D Salient Object Detection and Beyond (IEEE TNNLS)

Preface

This repo contains the source code and prediction saliency maps of our RD3D and RD3D+. The latter is an extension of the former, which is lighter and more computationally efficient and accurate.

RD3D: RGB-D Salient Object Detection via 3D Convolutional Neural Networks (PDF)

RD3D+: 3-D Convolutional Neural Networks for RGB-D Salient Object Detection and Beyond (PDF)

Update

🔥 Update 2022/09/15 🔥 Our work of RD3D+ is officially accepted and published in the IEEE Transactions on Neural Networks and Learning Systems now!

🔥 Update 2021/09/10 🔥 The Pytorch implementation of RD3D+ is now available! PDF is coming soon.

🔥 Update 2020/12/29 🔥 The Pytorch implementation of RD3D is now available!

Dataset

• Datasets in use:

  • NJU2K (1,985 pairs)
  • NLPR (1,000 pairs)
  • STERE (1,000 pairs)
  • DES/RGBD135 (135 pairs)
  • SIP (929 pairs)
  • DUTLF-D (1,200 pairs)
  • RedWeb-S (1,000 pairs)

  More information and downloading links of the former six datasets can be found in page, and the RedWeb-S can be downloaded from this project page.

💡Important tips💡

• 1485 paired RGB and depth images from NJU2K and 700 pairs from NLPR are used for training, while the remaining pairs are used for testing.
• On the DUTLF-D, however, additional 800 pairs from it are used for training and the rest 400 pairs are used for testing.
• In summary, our training set contains 2,185 pairs except when testing is conducted on DUTLF-D. More details can be found in the paper that will be released soon.

Usage

Repo clone

git clone https://github.com/PPOLYpubki/RD3D.git
cd RD3D

Prerequisites

Required packages are listed below:

• Ubuntu 16.04
• python=3.6
• pytorch>=1.6
• torchvision with pillow<7
• cuda>=10.1
• others: pip install termcolor opencv-python tensorboard

Inference

• Download the pre-trained weights and save them as ./model_path/RD3D.pth and ./model_path/RD3D_plus.pth.

  • RD3D Baidu Cloud, Fetch code: yoyj,Google Drive
  • RD3D+ Baidu Cloud, Fetch code: 7d3g,Google Drive

• Make sure your testing dataset be in ./data_path/ and run the following commands for inference:

  • On datasets except for the DUTLF-D.

  # RD3D
  python test.py --model RD3D --model_path ./model_path/RD3D.pth --data_path ./data_path/ --save_path ./save/all_results/ 
  # RD3D+
  python test.py --model RD3D_plus --model_path ./model_path/RD3D_plus.pth --data_path ./data_path/ --save_path ./save/all_results/

  • In particular, as what was mentioned in the Important tips, we also provide pre-trained weights of RD3D (Baidu Cloud, Fetch code: enza),(Google Drive) and pre-trained weights of RD3D+ (Baidu Cloud, Fetch code: 1lfc),(Google Drive) for the DUTLF-D case. Specifically, run the following command to test on the DUTLF-D:

  # RD3D
  python test.py --model RD3D --model_path ./pth/RD3D_DUTLF-D.pth --data_path ./data_path/ --save_path ./save/all_results/ 
  # RD3D+
  python test.py --model RD3D_plus --model_path ./pth/RD3D_plus_DUTLF-D.pth --data_path ./data_path/ --save_path ./save/all_results/ 

• All of our training processes are actually based on multiple GPUs. However, we have modified the key of some pre-trained weights, so please follow our command here for inference, otherwise there will be an error.

Training

• By default, make sure the training datasets be in the folder ./data_path/.

• Run the following command for training (Note that the model_name below can be either RD3D or RD3D_plus):

  python train.py --model [model_name] --data_dir ./data_path/

• Note that for researchers training with multiple GPUs, remember to add --multi_load to the inference command during testing.

Evaluation

We follow the authors of the SINet to conduct evaluations on our testing results.

We provide complete and fair one-key evaluation toolbox for benchmarking within a uniform standard. Please refer to this link for more information: Matlab version: https://github.com/DengPingFan/CODToolbox Python version: https://github.com/lartpang/PySODMetrics

Result

• Qualitative performance

  Quantitative RGB-D SOD results in terms of S-measure (S_α), maximum F-measure (F_β^max), maximum E-measure (E_Φ^max) and mean absolute error (MAE). Seven datasets are employed. For brevity, values in the table below are in the form of RD3D|RD3D+.

  Dataset	Sα	Fβmax	EΦmax	MAE
  NJU2K	0.916|0.928	0.914|0.928	0.947|0.955	0.036|0.033
  NLPR	0.930|0.933	0.919|0.921	0.965|0.964	0.022|0.022
  STERE	0.911|0.914	0.906|0.905	0.947|0.946	0.037|0.037
  RGBD135	0.935|0.950	0.929|0.946	0.972|0.982	0.019|0.017
  DUTLF-D	0.932|0.936	0.939|0.945	0.960|0.964	0.031|0.030
  SIP	0.885|0.892	0.889|0.900	0.924|0.928	0.048|0.046
  ReDWeb-S	0.700|0.718	0.687|0.697	0.780|0.786	0.136|0.130

• Downloading links of our result saliency maps:

  • RD3D: Baidu Cloud (Fetch code: am16), Google Drive
  • RD3D+: Baidu Cloud (Fetch code: hwna), Google Drive

Benchmark RGB-D SOD

The complete RGB-D SOD benchmark can be found in this page.

Citation

Please cite our work if you find them useful:

@inproceedings{chen2021rgb,
	title={RGB-D Salient Object Detection via 3D Convolutional Neural Networks},
	author={Chen, Qian and Liu, Ze and Zhang, Yi and Fu, Keren and Zhao, Qijun and Du, Hongwei},
	booktitle={Proceedings of the AAAI Conference on Artificial Intelligence},
	volume={35},
	number={2},
	pages={1063--1071},
	year={2021}
    }