Two-stage Depth Video Recovery with Spatiotemporal Coherence

This paper proposes a practical two-stage method to enhance the depth channel of low-quality RGB-D videos. At the first stage of the proposed method, we select several key frames from an input RGB-D video and recover them with a new MRF-regularized low-rank matrix completion method. At the second stage, we introduce a novel Laplacian smoothing algorithm to smoothly expand the recovered key depth frames to other in-between frames. With associated weights extracted from color images and a depth confidence constraint, we are able to recover each in-between frame faithfully and guarantee long-range temporal consistency. Experiments show that our method can generate high-quality and spatiotemporally coherent RGB-D videos for a wide range of scene configurations and achieve the state-of-the-art performance. Several applications further validate the effectiveness of the proposed method.