No abstract

InputHED Ours Input HED Ours Figure 1: Automatic contour drawing generation for images in the wild. Different from traditional edge or boundary detectors [48], our method predicts the most salient contours in images and reflects imperfections in ground truth human drawing, e.g. the ceiling is not perfectly straight in the right example as a novice drawer would draw it. Right photo by ostap25stock.adobe.com. AbstractEdges, 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 finetunes 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.

No abstract

No abstract

No abstract