Microsoft Research UK Ltd., Cambridge, UK.
IEEE Trans Pattern Anal Mach Intell. 2010 Mar;32(3):416-30. doi: 10.1109/TPAMI.2008.298.
Stereo matching algorithms conventionally match over a range of disparities sufficient to encompass all visible 3D scene points. Human vision, however, works over a narrow band of disparities-Panum's fusional band-whose typical range may be as little as 1/20 of the full range of disparities for visible points. Only points inside the band are fused visually; the remainder of points are seen diplopically. A probabilistic approach is presented for dense stereo matching under the Panum band restriction. It is shown that existing dense stereo algorithms are inadequate in this problem setting and the main problem is segmentation, marking the image into the areas that fall inside the band. An approximation is derived that makes up for missing out-of-band information with a "proxy" based on image autocorrelation. It is shown that the Panum Proxy algorithm achieves accuracy close to what can be obtained when the full disparity band is available, and with gains of between one and two orders of magnitude in computation time. There are also substantial gains in computation space. Panum band processing is also demonstrated in an active stereopsis framework.
立体匹配算法通常在足以包含所有可见 3D 场景点的视差范围内进行匹配。然而，人类视觉仅在视差的窄带内工作-普纳融合带-其典型范围可能只有可见点的全视差范围的 1/20。只有带内的点在视觉上融合；其余的点则是双重视点。本文提出了一种在普纳带限制下进行密集立体匹配的概率方法。结果表明，现有的密集立体算法在这种问题设置下是不够的，主要问题是分割，即将图像标记为落在带内的区域。得出了一种近似值，该近似值使用基于图像自相关的“代理”来弥补带外信息的缺失。结果表明，普纳代理算法在可用全视差带的情况下能够达到接近的精度，并且在计算时间上提高了一到两个数量级。在计算空间上也有很大的收益。普纳带处理也在主动立体视觉框架中得到了展示。
