A depth map upsampling method using depth local features is proposed. Depth discontinuity and depth variance are extracted from a low-resolution depth map and a colour image as the depth local features. They are incorporated into an energy function for the Markov random field (MRF)-based depth upsampling. The high-resolution depth map is obtained by optimising the energy function using belief propagation. The experimental results show that the proposed method outperforms other depth upsampling approaches in terms of the bad pixel rate. Introduction: Three-dimensional (3D) video provides immersive sense using images from dense multiple viewpoints. However, since the capturing of the dense multiple viewpoints is difficult due to the mechanical limitation, depth maps are required to synthesise intermediate viewpoint images. Thus, acquiring high-quality depth maps is important in 3D video and related applications. In recent years, depth sensors based on the time-of-flight technique have been used to acquire the depth map of the scene. Although they generate the depth map in real-time, lowresolution output is one of the critical drawbacks. To overcome this lowresolution output, depth map upsampling methods such as filter-based [1, 2] and Markov random field (MRF)-based [3] approaches have been proposed. However, the filter-based methods can cause an oversmooth problem at the depth discontinuity regions, and also the MRF-based method can occur with error propagation during the optimisation process. In this Letter, a new depth map upsampling method to reduce such problems is presented.
Kang et al. REF proposed the depth map upsampling method with low-resolution depth map and a color image, which provides reference for feature extraction of stereo vision image.
Depth map upsampling using depth local features
Electronics Letters
Mathematics