Toward General and Robust Visible Surface Reconstruction

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This is the first year of a three-year continuing award. The research investigates a new approach to surface reeconstruction in computer vision. Surface reconstruction, the process of recovering descriptions of the visible surfaces in a scene from a noisy and perhaps sparse set of depth points, plays a fundamental role in such robotic tasks as object modeling, grasp planning and navigation. Standard approaches have two limitations: they assume that there are no extreme errors in the data; and they assume that the visible surface can be accurately modeled with a single piecewise continuous function of the image coordinates. These limitations severely restrict the applicability of present surface reconstruction techniques. The new approach overcomes these problems using a two phase technique: (1) a local estimation phase that produces polynomial surface patches in regions throughout the image, including possibly overlapping surfaces, and (2) a global refinement phase that links and refines mutually consistent local estimates.