Noname manuscript No. (will be inserted by the editor) Monocular Extraction of 2.1D Sketch Using Constrained Convex Optimization

Abstract This paper presents an approach to estimating the 2.1D sketch frommonocular, low-level visual cues. We use a low-level segmenter to partition the image into regions, and, then, estimate their 2.1D sketch, subject to figure-ground and similarity constraints between neighboring regions. The 2.1D sketch assigns a depth ordering to image regions which are expected to correspond to objects and surfaces in the scene. This is cast as a constrained convex optimization prob-lem, and solved within the optimization transfer framework. The optimization objective takes into account the curvature and convexity of parts of region boundaries, appearance, and spatial layout properties of regions. Our new optimiza-tion transfer algorithm admits a closed-form expression of the duality gap, and thus allows explicit computation of the achieved accuracy. The algorithm is efficient with quadratic complexity in the number of constraints between image re-gions. Quantitative and qualitative results on challenging, real-world images of Berkeley segmentation, Geometric Con-text, and Stanford Make3D datasets demonstrate our high accuracy, efficiency, and robustness