View Selection Strategies for Multi-View, Wide-Baseline Stereo

Recovering 3D depth information from two or more 2D intensity images is a long standing problem in the computer vision community. This paper presents a multi-baseline, coarse-to-fine stereo algorithm which utilizes any number of images (more than 2) and multiple image scales to recover 3D depth information. Several "view-selection strategies" are introduced for combining information across the multi-baseline and across scale space. The control strategies allow us to exploit, maximally, the benefits of large and small baselines and mask sizes while minimizing errors. Results of recovering 3D depth information from a human head are presented. The resulting depth maps are of good accuracy with a depth resolution of approximately 5mm.