The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model
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.
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Cites
Golub, G. (2012) · doi:10.56021/9781421407944 · Cited by 34,505
A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms
Scharstein, D., & Szeliski, R. (2002) · International Journal of Computer Vision · doi:10.1023/a:1014573219977 · Cited by 6,740
He, K., Sun, J., & Tang, X. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15549-9_1 · Cited by 1,565
An Image Inpainting Technique Based on the Fast Marching Method
Telea, A. (2004) · Journal of Graphics Tools · doi:10.1080/10867651.2004.10487596 · Cited by 1,421
A large-scale hierarchical multi-view RGB-D object dataset
Lai, K., Bo, L., Ren, X., & Fox, D. (2011) · 2011 IEEE International Conference on Robotics and Automation · doi:10.1109/icra.2011.5980382 · Cited by 1,316
Szeliski, R., Zabih, R., Scharstein, D., Veksler, O., Kolmogorov, V., Agarwala, A., Tappen, M., & Rother, C. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.70844 · Cited by 966
Kopf, J., Cohen, M. F., Lischinski, D., & Uyttendaele, M. (2007) · ACM SIGGRAPH 2007 papers · doi:10.1145/1275808.1276497 · Cited by 872
Spatial-Depth Super Resolution for Range Images
Yang, Q., Yang, R., Davis, J., & Nister, D. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383211 · Cited by 745
Robust moving least-squares fitting with sharp features
Fleishman, S., Cohen-Or, D., & Silva, C. T. (2005) · ACM Transactions on Graphics · doi:10.1145/1073204.1073227 · Cited by 580
An Application of Markov Random Fields to Range Sensing
Diebel, J. R., & Thrun, S. (2005) · Cited by 535
Cited by
Learning Depth with Convolutional Spatial Propagation Network
Cheng, X., Wang, P., & Yang, R. (2018) · arXiv · doi:10.48550/arxiv.1810.02695 · Cited by 345
“Recently, deep learning based enhancement yields impressive results on super resolution of both images [61], [62] and depths [63], [64], [65], [66].”
BackgroundLearning Depth with Convolutional Spatial Propagation Network
Cheng, X., Wang, P., & Yang, R. (2020) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2019.2947374 · Cited by 341
Metadata
- Authors
- Yang, J., Ye, X., Li, K., Hou, C., & Wang, Y.
- Venue
- IEEE Transactions on Image Processing
- Published
- 2014-08
- Volume
- 23
- Issue
- 8
- Pages
- 3443-3458
- ISSN
- 1057-7149 (ISSN-L)
- 1941-0042
- OpenAlex
- W1994184495
- Cited by
- 310
- References
- 36
- FWCI
- 26.71
- Type
- journal article
- Language
- en
- Open access
- closed
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- Yang, J., Ye, X., Li, K., Hou, C., & Wang, Y. (2014). Color-Guided Depth Recovery From RGB-D Data Using an Adaptive Autoregressive Model. IEEE Transactions on Image Processing. https://doi.org/10.1109/tip.2014.2329776
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