Statistical methods for inter-view depth enhancement

This paper briefly presents and evaluates recent advances in statistical methods for improving inter-view inconsistency in multiview depth imagery. View synthesis is vital in free-viewpoint television in order to allow viewers to move freely in a dynamic scene. Here, depth image-based rendering plays a pivotal role by synthesizing an arbitrary number of novel views by using a subset of captured views and corresponding depth maps only. Usually, each depth map is estimated individually at different viewpoints by stereo matching and, hence, shows lack of inter-view consistency. This lack of consistency affects the quality of view synthesis negatively. This paper discusses two different approaches to enhance the inter-view depth consistency. The first one uses generative models based on multiview color and depth classification to assign a probabilistic weight to each depth pixel. The weighted depth pixels are utilized to enhance depth maps. The second one performs inter-view consistency testing in depth difference space to enhance the depth maps at multiple viewpoints. We comparatively evaluate these two methods and discuss their pros and cons for future work.
QC 20150109