NDLI: Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model

Content Provider	IEEE Xplore Digital Library
Author	Jingyu Yang Xinchen Ye Kun Li Chunping Hou Yao Wang
Copyright Year	1992
Abstract	This paper proposes an adaptive color-guided autoregressive (AR) model for high quality depth recovery from low quality measurements captured by depth cameras. We observe and verify that the AR model tightly fits depth maps of generic scenes. The depth recovery task is formulated 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. We analyze the stability of our method from a linear system point of view, and design a parameter adaptation scheme to achieve stable and accurate depth recovery. Quantitative and qualitative evaluation compared with ten state-of-the-art schemes show the effectiveness and superiority of our method. Being able to handle various types of depth degradations, the proposed method is versatile for mainstream depth sensors, time-of-flight camera, and Kinect, as demonstrated by experiments on real systems.
Sponsorship	IEEE Signal Processing Society
Page Count	16
File Size	8677636
Starting Page	3443
Ending Page	3458
File Format	PDF
ISSN	10577149
Volume Number	23
Issue Number	8
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2014-01-01
Publisher Place	U.S.A.
Access Restriction	One Nation One Subscription (ONOS)
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Cameras Color Image color analysis Adaptation models Degradation Image resolution Data models RGB-D camera Depth recovery (upsampling, inpainting, denoising) autoregressive model
Content Type	Text
Resource Type	Article
Subject	Computer Graphics and Computer-Aided Design Software

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