Edges, boundaries and contours are important subjects of study in both
computer graphics and computer vision. On one hand, they are the 2D elements
that convey 3D shapes, on the other hand, they are indicative of occlusion
events and thus separation of objects or semantic concepts. In this paper, we
aim to generate contour drawings, boundary-like drawings that capture the
outline of the visual scene. Prior art often cast this problem as boundary
detection. However, the set of visual cues presented in the boundary detection
output are different from the ones in contour drawings, and also the artistic
style is ignored. We address these issues by collecting a new dataset of
contour drawings and proposing a learning-based method that resolves diversity
in the annotation and, unlike boundary detectors, can work with imperfect
alignment of the annotation and the actual ground truth. Our method surpasses
previous methods quantitatively and qualitatively. Surprisingly, when our model
fine-tunes on BSDS500, we achieve the state-of-the-art performance in salient
boundary detection, suggesting contour drawing might be a scalable alternative
to boundary annotation, which at the same time is easier and more interesting
for annotators to draw.

本文提出了一种基于学习的方法，采用新的数据集进行轮廓图的生成和处理，可以精确定位视觉场景的轮廓和边界，相对于传统的边界检测方法有更好的性能表现，并且在 BSDS500 上取得了最新的性能成果。