Depth map processing with iterative joint multilateral filtering

Depth maps estimated using stereo matching between frames from different video views typically exhibit false contours and noisy artifacts around object boundaries. In this paper, iterative joint multilateral filtering is proposed to deal with these artifacts. The proposed filter consists of multiple filter kernels. Knowing that the estimated depth maps are erroneous, besides the kernels which measure the proximity of depth samples and the similarity between depth sample values, we further develop kernels which measure similarity between the corresponding video pixel values. To increase reliability, these novel kernels operate on the color (RGB) domain instead of only on the luminance domain. Furthermore, the filter shapes are designed to adapt brightness variations. Finally, to tackle large misalignment between boundaries in depth maps and in the corresponding video frames, iterative approach is utilized. Our results demonstrate that the proposed method can significantly improve the boundaries in depth maps and can reduce false contours. With the processed depth maps, it is observed that the quality of object boundaries in synthesized views can be improved.