Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model
Abstract
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 nonlocal similarity in the accompanied high quality color image. Experimental results show that our method outperforms existing state-of-the-art schemes, and is versatile for both mainstream depth sensors: ToF camera and Kinect.
Cite
Text
Yang et al. "Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model." European Conference on Computer Vision, 2012. doi:10.1007/978-3-642-33715-4_12Markdown
[Yang et al. "Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model." European Conference on Computer Vision, 2012.](https://mlanthology.org/eccv/2012/yang2012eccv-depth/) doi:10.1007/978-3-642-33715-4_12BibTeX
@inproceedings{yang2012eccv-depth,
title = {{Depth Recovery Using an Adaptive Color-Guided Auto-Regressive Model}},
author = {Yang, Jingyu and Ye, Xinchen and Li, Kun and Hou, Chunping},
booktitle = {European Conference on Computer Vision},
year = {2012},
pages = {158-171},
doi = {10.1007/978-3-642-33715-4_12},
url = {https://mlanthology.org/eccv/2012/yang2012eccv-depth/}
}