A Non-local Low-rank and Sparsity based Framework for Depth Map Inpainting
Sankaraganesh Jonna, Moushumi Medhi · 2021
Depth is an important cue along with RGB data in many computer vision applications. Depth maps captured by most of the mainstream depth sensors are noisy with a lot of missing depth information causing annoying visual artifacts. In this work, we proposed a joint framework for color guided depth map inpainting. We exploit the redundancy of RGBD data by integrating non-local low-rank patch regularization along with local structural information in an optimization framework. The proposed non-local patch-based regularization prior in addition to a complementary local smoothness prior facilitate robust recovery of the sharp depth discontinuities and the missing depth information.