RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth, in ICCV 2021 (oral)

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Deep Learning deep-learning edge-detection
Overview

RINDNet

RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth
Mengyang Pu, Yaping Huang, Qingji Guan and Haibin Ling
ICCV 2021 (oral)

Please refer to supplementary material (code:p86d) (~60M) for more results.

Benchmark --- 🔥 🔥 BSDS-RIND 🔥 🔥

BSDS-RIND is the first public benchmark that dedicated to studying simultaneously the four edge types, namely Reflectance Edge (RE), Illumination Edge (IE), Normal Edge (NE) and Depth Edge (DE). It is created by carefully labeling images from the BSDS500. The datasets can be downloaded from:

Abstract

As a fundamental building block in computer vision, edges can be categorised into four types according to the discontinuity in surface-Reflectance, Illumination, surface-Normal or Depth. While great progress has been made in detecting generic or individual types of edges, it remains under-explored to comprehensively study all four edge types together. In this paper, we propose a novel neural network solution, RINDNet, to jointly detect all four types of edges. Taking into consideration the distinct attributes of each type of edges and the relationship between them, RINDNet learns effective representations for each of them and works in three stages. In stage I, RINDNet uses a common backbone to extract features shared by all edges. Then in stage II it branches to prepare discriminative features for each edge type by the corresponding decoder. In stage III, an independent decision head for each type aggregates the features from previous stages to predict the initial results. Additionally, an attention module learns attention maps for all types to capture the underlying relations between them, and these maps are combined with initial results to generate the final edge detection results. For training and evaluation, we construct the first public benchmark, BSDS-RIND, with all four types of edges carefully annotated. In our experiments, RINDNet yields promising results in comparison with state-of-the-art methods.

Code and Main results ----- Coming Soon...

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.
Comments
  • There is No .mat format file of gt in the BSDS-RINED test set

    There is No .mat format file of gt in the BSDS-RINED test set

    Excuse me, when I retrained the network and planned to evaluate the prediction results, I found that there was no .mat format file of the test set, and the ground truth map of the test set only had a .png format file. I tried to generate the .mat file by myself from the .png file, but encountered some unknown errors during the evaluation. Can you please release the ground truth map in .mat format? Thank you very much for your help.

    opened by zhoujh2020 3
  • concerning to your qualitative results

    concerning to your qualitative results

    the output of the model is a probability map , how can we visualize this map as your paper shown ? or what is the threshold number you use it to judge the pixel is edge or not ? is it 0.5 ? or ?

    opened by Daniellli 0
  • unable to download pretrained models

    unable to download pretrained models

    Hi,

    I am unable to download pretrained models. The website is asking me to sign in/up with baidu cloud(to downlooad files) but as I do not live in China I cannot able to sign up/in.

    Can you provide me some other links to download pretrained models?

    Thanks

    opened by hellfire504 0
  • How to evaluate generic edge？

    How to evaluate generic edge？

    There is no .mat file of generic edge. Should I generate it myself? (For example, compress the channel of the target to 1, as shown in your train_edge.py file) Or can you provide the .mat files.

    opened by zhoujh2020 0
  • How to do direct inference using pre-trained models

    How to do direct inference using pre-trained models

    I want to run your pre-trained model on my own images directly. But there is no option or parameter to inference in your evaluate codes. Kindly, please help me out.

    opened by shubham12tomar 1
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Mengyang Pu
Mengyang Pu
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