Depth map super-resolution via Markov Random Fields without texture-copying artifacts

The use of time-of-flight sensors enables the record of full-frame depth maps at video frame rate, which benefits a variety of 3D image or video processing applications. However, such depth maps are typically corrupted by noise and with limited resolution. In this paper, we present a learning-based depth map super-resolution framework by solving a MRF labeling optimization problem. With the captured depth map and the associated high-resolution color image, our proposed method exhibits the capability of preserving the edges of range data while suppressing the artifacts of texture copying due to color discontinuities. Quantitative and qualitative experimental results demonstrate the effectiveness and robustness of our approach over prior depth map upsampling works.