RINDNet-plusplus

RINDNet++: Edge Detection for Discontinuity in Reflectance, Illumination, Normal, and Depth
Mengyang Pu, Yaping Huang, Qingji Guan, Zhihao Liu, and Haibin Ling
IJCV 2025

Annotation

Annotation Data is available in GoogleDrive.

Under various illumination environments

The collected data under various illumination environments is available in GoogleDrive.

Usage

Training

1. Clone this repository to local

git clone https://github.com/MengyangPu/RINDNet-plusplus.git

2. Download the augmented data to the local folder /data

3. run train

python train_RINDNet_plusplus_80k.py
or
python train_RINDNet_plusplus_edge_80k.py

more train files (train_modelname80k.py and trainmodelname_edge_80k.py) in /train_tools

4. Note: The imagenet pretrained vgg16 pytorch model for BDCN can be downloaded in [vgg16.pth](link: https://pan.baidu.com/s/10Tgjs7FiAYWjVyVgvEM0mA) code: ab4g. The imagenet pretrained vgg16 pytorch model for HED can be downloaded in 5stage-vgg.py36pickle code: 9po1.

Acknowledgments

• The work is partially done while Mengyang was at Stony Brook University.
• We thank the anonymous reviewers for valuable and inspiring comments and suggestions.
• Thanks to the previous open-sourced repo:
  HED-pytorch
  RCF-pytorch
  BDCN
  DexiNed
  DFF
  pytorch-deeplab-xception
  DOOBNet-pytorch