Research in Acquisition of Three Dimensional Information

这项中低级计算机视觉研究的主要目标是为机器人视觉系统开发获取3-D感官数据的新算法。这项工作将研究：--新的被动视觉技术，用于获得关于三维世界中表面的表面方向和曲率信息；候选技术包括：基于偏振的立体、彩色立体、直接光度曲率计算和镜面形状计算--提高了立体匹配和基于深度和方向信息的曲面重建技术的精度和效率。这些改进将源于理论上对匹配、曲面重建和曲面分割的统一处理。工作将包括理论分析和实验验证。它与以前的研究不同之处在于，新的算法将从立体声过程的一般理论中派生出来，这些理论将作为本研究的一部分完成。新算法将使用不同类型的信息(例如，在不同光源极化下的图像)，或者在更现实的假设下处理标准信息(例如，视差表面应该是平滑的，但不一定是恒定的或平面的)。

The main goal of this research in low- and middle-level computer vision is to develop new algorithms for the acquisition of 3-D sensory data for robotic-vision systems. The work will investigate: -- new passive vision techniques for obtaining surface orientation and curvature information about surfaces in the 3-D world; candidate techniques include: polarization-based stereo, color stereo, direct photometric curvature calculation, and shape-from-specularity -- improvements in both the accuracy and efficiency of stereo-matching and surface-reconstruction techniques from depth and orientation information. These improvements will result from a theoretically unified treatment of matching, surface reconstruction, and surface segmentation. The work will include theoretical analysis and experimental verification. It differs from previous research in that the new algorithms are to be derived from the general theories of stereo processes, which are to be finalized as part of this research. The new algorithms will either use different types of information (e.g., images under varied source polarization) or treat standard information under more-realistic assumptions (e.g., that the disparity surfaces should be smooth, but not necessarily constant or planar).