Translation-Symmetry-Based Perceptual Grouping With Applications To Urban Scenes

An important finding in our understanding of the human vision system is perceptual grouping, the mechanism by winch visual elements are organized into coherent groups. Though grouping is generally acknowledged to be a crucial component of the mid-level visual system, in computer vision there is a scarcity of mid-level cues due to computational difficulties in constructing feature detectors for such cues. We propose a novel mid-level visual feature detector where the visual elements are grouped based on the 2D translation subgroup of a wallpaper pattern. Different from previous state-of-the-art lattice detection algorithms for near-regular wallpaper patterns, our proposed method can detect multiple, semantically relevant 2D lattices in a scene simultaneously, achieving an effective translation-symmetry-based segmentation. Our experimental results on urban scenes demonstrate the use of translation-symmetry for building facade super-resolution and orientation estimation from a single view.