Authors: Minhyeok Lee, Chaewon Park, Suhwan Cho, Sangyoun Lee
This repository provides code for paper "SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection" accepted by the ECCV 2022 conference.
Our paper can be found [arXiv]
Download the train and test dataset from Google Drive.
For the superpixel algorithm we use fast_slic. You can install it like this:
pip install fast_slic
- First, clone this repository.
git clone https://github.com/Hydragon516/SPSN
- Edit config.py. The data root path option and GPU index should be modified.
- Train the model.
python3 train.py
When training is complete, the prediction results for the test set are saved in the ./log folder. Two popular evaluation toolboxes are available. (Matlab version: https://github.com/DengPingFan/CODToolbox Python version: https://github.com/lartpang/PySODMetrics)
./log directory structure
.
├── root
└── log/
└── 2022-xx-xx xx:xx:xx/
├── model/
│ └── best_model.pth
├── result/
│ ├── gt/ # ground truth images
│ │ ├── NJU2K
│ │ ├── NLPR
│ │ ├── DES
│ │ ├── SIP
│ │ └── STERE
│ ├── pred/ # predicted mask images (only mask)
│ │ ├── NJU2K
│ │ ├── NLPR
│ │ ├── DES
│ │ ├── SIP
│ │ └── STERE
│ └── total/ # includes RGB, depth, GT, superpixel sampling maps, prediction mask, and more
│ ├── NJU2K
│ ├── NLPR
│ ├── DES
│ ├── SIP
│ └── STERE
└── train/
└── config.py
An example of the resulting image is shown below.
- A : RGB image
- B : Depth map
- C : Pred map
- D : GT
- E : Pred superpixel map from RGB
- F : GT superpixel map from RGB
- G : Pred superpixel map from depth
- H : GT superpixel map from depth
The prediction mask results of our proposed model can be found here.
@article{lee2022spsn,
title={SPSN: Superpixel Prototype Sampling Network for RGB-D Salient Object Detection},
author={Lee, Minhyeok and Park, Chaewon and Cho, Suhwan and Lee, Sangyoun},
journal={arXiv preprint arXiv:2207.07898},
year={2022}
}