  Edge detection is a fundamental technique in various computer vision tasks.
Edges are indeed effectively delineated by pixel discontinuity and can offer
reliable structural information even in textureless areas. State-of-the-art
heavily relies on pixel-wise annotations, which are labor-intensive and subject
to inconsistencies when acquired manually. In this work, we propose a novel
self-supervised approach for edge detection that employs a multi-level,
multi-homography technique to transfer annotations from synthetic to real-world
datasets. To fully leverage the generated edge annotations, we developed
SuperEdge, a streamlined yet efficient model capable of concurrently extracting
edges at pixel-level and object-level granularity. Thanks to self-supervised
training, our method eliminates the dependency on manual annotated edge labels,
thereby enhancing its generalizability across diverse datasets. Comparative
evaluations reveal that SuperEdge advances edge detection, demonstrating
improvements of 4.9% in ODS and 3.3% in OIS over the existing STEdge method on
BIPEDv2.
