Extracting View-Dependent Depth Maps from a Collection of Images

摘要

Stereo correspondence algorithms typically produce a single depth map. In addition to the usual problems of occlusions and textureless regions, such algorithms cannot model the variation in scene or object appearance with respect to the viewing position. In this paper, we propose a new representation that overcomes the appearance variation problem associated with an image sequence. Rather than estimating a single depth map, we associate a depth map with each input image (or a subset of them). Our representation is motivated by applications such as view interpolation and depth-based segmentation for model-building or layer extraction. We describe two approaches to extract such a representation from a sequence of images.