Kinect Depth Inpainting via Graph Laplacian with TV21 Regularization
Shaoguo Liu, Ying Wang, Haibo Wang, Chunhong Pan · 2013
Depth maps provided by Microsoft Kinect often contain large dark holes around depth boundaries and occasional missing pixels in non-occluded regions, as well as noise, which prevent their further usage in real-world applications. In this paper, we present a graph Laplacian based framework to restore missing pixels based on the strong correlation between color image and depth map. To preserve sharp edges and remove noise, the TV21 (Total Variation) prior of depth maps is then integrated as an additional regularizer to the framework. Finally, an efficient and effective iterative optimization method with a closed-form solution at each iteration is presented to address this issue. Experiments conducted on both real scene images and synthetic images demonstrate that our approach gives better performance than commonly-used depth in painting schemes.