Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model
Top Cited Papers
- 1 August 2014
- journal article
- research article
- Published by Institute of Electrical and Electronics Engineers (IEEE) in IEEE Transactions on Image Processing
- Vol. 23 (8), 3443-3458
- https://doi.org/10.1109/tip.2014.2329776
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.Keywords
Funding Information
- National Natural Science Foundation of China (61372084, 61302059, 61228104, 91320201)
- Program for New Century Excellent Talents in University (NCET-11-0376)
- Ph.D. Programs Foundation through the Ministry of Education of China (20110032110029)
- Tianjin Research Program of Application Foundation and Advanced Technology (12JCYBJC10300, 13JCQNJC03900)
This publication has 38 references indexed in Scilit:
- Efficient spatio-temporal hole filling strategy for Kinect depth mapsPublished by SPIE-Intl Soc Optical Eng ,2012
- Fusion of range and color images for denoising and resolution enhancement with a non-local filterComputer Vision and Image Understanding, 2010
- Time‐of‐Flight Cameras in Computer GraphicsComputer Graphics Forum, 2010
- Theoretical and experimental error analysis of continuous-wave time-of-flight range camerasOptical Engineering, 2009
- Denoising and interpolation of noisy Bayer data with adaptive cross-color filtersPublished by SPIE-Intl Soc Optical Eng ,2008
- Increasing depth lateral resolution based on sensor fusionInternational Journal of Intelligent Systems Technologies and Applications, 2008
- Robust moving least-squares fitting with sharp featuresACM Transactions on Graphics, 2005
- An Image Inpainting Technique Based on the Fast Marching MethodJournal of Graphics Tools, 2004
- The application of auto–regressive time series modelling for the time–frequency analysis of civil engineering structuresEngineering Structures, 2001
- Nested auto-regressive processes for MPEG-encoded video traffic modelingIEEE Transactions on Circuits and Systems for Video Technology, 2001