Explicit Edge Inconsistency Evaluation Model for Color-guided Depth Map Enhancement

Structure guided fusion for depth map inpainting

0167-8655/$ see front matter 2012 Elsevier B.V. A http://dx.doi.org/10.1016/j.patrec.2012.06.003 ⇑ Corresponding author. E-mail addresses: [email protected] (F. Qi), gmshi@x 1 Principal corresponding author. Depth acquisition becomes inexpensive after the revolutionary invention of Kinect. For computer vision applications, depth maps captured by Kinect require additional processing to fill up mi...

Guided Filters for Depth Image Enhancement

Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model

This paper proposes an adaptive color-guided auto-regressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We formulate the depth recovery task into a minimization of AR prediction errors subject to measurement consistency. The AR predictor for each pixel is constructed according to both the local correlation in the initial depth map and the...

Fast Depth Map Upsampling using Edge Information

In this paper, we propose a new depth map upsampling method to increase the depth image resolution using edge information. Although the joint bilateral upsampling (JBU) method expands the resolution of the depth map using two weighting functions, the complexity of JBU is relatively high. In the proposed upsampling method, we reduce the complexity of depth map upsmapling operation using a color ...

Depth Map Boundary Enhancement Using Random Walk

Depth information is essential for depth image-based rendering (DIBR), which is one of the rendering processes for the virtual view using a color image and its corresponding depth map. Although there are several depth estimation methods, more accurate depth estimation is still required. Since inaccurate depth information along object boundaries causes serious rendering errors, depth boundary in...