Kinect depth restoration via energy minimization with TV21 regularization
Shaoguo Liu, Ying Wang, Jue Wang, Haibo Wang, Jixia Zhang, Chunhong Pan · 2013
Depth maps generated by Kinect cameras often contain a significant amount of missing pixels and strong noise, limiting their usability in many computer vision applications. We present a new energy minimization method to fill the missing regions and remove noise in a depth map, by exploiting the strong correlation between color and depth values in local image neighborhoods. To preserve sharp edges and remove noise from the depth map, we propose to add a TV21regularization term into the energy function. Finally, we show how to effectively minimize the total energy using an alternating optimization approach. Experimental results show that the proposed method outperforms commonly-used depth inpainting approaches.