Toward General and Robust Visible Surface Reconstruction

今年是连续三年获奖的第一年。 的 研究调查了一种新的表面重建方法， 计算机视觉表面重建，恢复的过程 描述的可见表面在一个场景中，从一个嘈杂的， 也许是稀疏的一组深度点，在 诸如对象建模、抓取规划和 导航 标准方法有两个局限性： 数据中没有极端错误;他们假设， 可见表面可以用单个 图像坐标的分段连续函数。 这些 这些局限性严重限制了现有表面的适用性 重建技术。 新方法克服了这些 使用两阶段技术的问题：（1）局部估计阶段 在整个区域中生成多项式曲面片， 图像，包括可能重叠的表面，以及（2）全局 细化阶段，链接和细化相互一致的本地 估算

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.