  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.
