Shape Completion from a Single RGBD Image

We present a novel approach for constructing a complete 3D model for an object from a single RGBD image. Given an image of an object segmented from the background, a collection of 3D models of the same category are non-rigidly aligned with the input depth, to compute a rough initial result. A volumetric-patch-based optimization algorithm is then performed to refine the initial result to generate a 3D model that not only is globally consistent with the overall shape expected from the input image but also possesses geometric details similar to those in the input image. The optimization with a set of high-level constraints, such as visibility, surface confidence and symmetry, can achieve more robust and accurate completion over state-of-the art techniques. We demonstrate the efficiency and robustness of our approach with multiple categories of objects with various geometries and details, including busts, chairs, bikes, toys, vases and tables.