An adaptive background biased depth map hole-filling method for Kinect
Litong Feng, Lai-Man Po, Xuyuan Xu, Ka-Ho Ng, Terence Chun-Ho Cheung, Kwok-Wai Cheung · 2013
The launch of Kinect provides a convenient way to access the depth information in real time. However the depth map quality still needs to be enhanced for 3D visual applications. In this paper, an adaptive background biased depth map hole-filling method is proposed. First, depth holes caused by abnormal reflection are filled by color similarity in-painting, and a soft decision for color similarity checking is performed by the use of probabilities in random walks color segmentation. Afterwards it is assumed that the lost information in the rest of depth holes belongs to the background. The background depth information is extracted by automatic thresholding in the neighborhood of each hole. Depth holes are in-painted with the background information in their local neighborhood. Combination of color similarity in-painting and background biased in-painting is able to perform depth map hole-filling adaptively for different kinds of depth holes for Kinect. The hole-filling results and virtual view synthesis results show that the Kinect depth map quality can be improved significantly by the proposed method.