Break Ames Room Illusion: Depth from General Single Images

Photos compress 3D visual data to 2D. However, it is still possible to infer depth information even without sophisticated object learning. We propose a solution based on small-scale defocus blur inherent in optical lens and tackle the estimation problem by proposing a non-parametric matching scheme for natural images. It incorporates a matching prior with our newly constructed edgelet dataset using a non-local scheme, and includes semantic depth order cues for physically based inference. Several applications are enabled on natural images, including geometry based rendering and editing.