Online Temporally Consistent Indoor Depth Video Enhancement via Static Structure


In this paper, we propose a new method to online enhance the quality of a depth video based on the intermediary of a so-called static structure of the captured scene. The static and dynamic regions of the input depth frame are robustly separated by a layer assignment procedure, in which the dynamic part stays in the front while the static part fits and helps to update this structure by a novel online variational generative model with added spatial refinement. The dynamic content is enhanced spatially while the static region is otherwise substituted by the updated static structure so as to favor the long-range spatio-temporal enhancement. The proposed method both performs long-range temporal consistency on the static region and keeps necessary depth variations in the dynamic content. Thus, it can produce flicker-free and spatially optimized depth videos with reduced motion blur and depth distortion. Our experimental results reveal that the proposed method is effective in both static and dynamic indoor scenes and is compatible with depth videos captured by Kinect and time-of-flight camera. We also demonstrate that excellent performance can be achieved by the proposed method in comparison with the existing spatio-temporal approaches. In addition, our enhanced depth videos and static structures can act as effective cues to improve various applications, including depth-aided background subtraction and novel view synthesis, showing satisfactory results with few visual artifacts.

IEEE Transactions on Image Processing