Extracting View-Dependent Depth Maps from a Collection of Images

TLDR: A new representation is proposed that overcomes the appearance variation problem associated with an image sequence and simultaneously optimizes a set of self-consistent depth maps at multiple key-frames.

Abstract: 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. The first approach, which is more classical, computes the local depth map associated with each chosen reference frame independently. The novelty of this approach lies in its combination of shiftable windows, temporal selection, and graph cut optimization. The second approach simultaneously optimizes a set of self-consistent depth maps at multiple key-frames. Since multiple depth maps are estimated simultaneously, visibility can be modeled explicitly and disparity consistency imposed across the different depth maps. Results, which include a difficult specular scene example, show the effectiveness of our approach.