A revisit to MRF-based depth map super-resolution and enhancement

This paper presents a Markov Random Field (MRF)-based approach for depth map super-resolution and enhancement. Given a low-resolution or moderate quality depth map, we study the problem of enhancing its resolution or quality with a registered high-resolution color image. Different from the previous methods, this MRF-based approach is based on a novel data term formulation that fits well to the unique characteristics of depth maps. We also discuss a few important design choices that boost the performance of general MRF-based methods. Experimental results show that our proposed approach achieves high resolution depth maps at more desirable quality, both qualitatively and quantitatively. It can also be applied to enhance the depth maps derived with state-of-the-art stereo methods, resulting in the raised ranking based on the Middlebury benchmark.