RINDNet: Edge Detection for Discontinuity in Reflectance, Illumination, Normal and Depth

By Mengyang Pu, Yaping Huang, Qingji Guan, Haibin Ling. 2021

83 citations recorded by the source corpus.

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. Additional analysis is presented in supplementary material.

Subfields: Atomic and Molecular Physics, and Optics, Computer Vision and Pattern Recognition, Industrial and Manufacturing Engineering

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Atomic and Molecular Physics, and Optics

  1. Yong Jae Lee
  2. Haibin Ling

In Computer Vision and Pattern Recognition

  1. Yong Jae Lee
  2. Haibin Ling
  3. Haotian Liu
  4. Xueyan Zou
  5. Ming-Hsuan Yang
  6. Luc Van Gool
  7. Dacheng Tao
  8. Yaping Huang

In Industrial and Manufacturing Engineering

  1. Yong Jae Lee
  2. Haibin Ling

Citations in the corpus