Depth pixel clustering for consistency testing of multiview depth

This paper proposes a clustering algorithm of depth pixels for consistency testing of multiview depth imagery. The testing addresses the inconsistencies among estimated depth maps of real world scenes by validating depth pixel connection evidence based on a hard connection threshold. With the proposed algorithm, we test the consistency among depth values generated from multiple depth observations using cluster adaptive connection thresholds. The connection threshold is based on statistical properties of depth pixels in a cluster or sub-cluster. This approach can improve the depth information of real world scenes at a given viewpoint. This allows us to enhance the quality of synthesized virtual views when compared to depth maps obtained by using fixed thresholding. Depth-image-based virtual view synthesis is widely used for upcoming multimedia services like three-dimensional television and free-viewpoint television.