The DOI matches its Crossref record; 45 of the 45 references it cites were found in a registry or an index.
Fast cost-volume filtering for visual correspondence and beyond
Abstract
Many computer vision tasks can be formulated as labeling problems. The desired solution is often a spatially smooth labeling where label transitions are aligned with color edges of the input image. We show that such solutions can be efficiently achieved by smoothing the label costs with a very fast edge preserving filter. In this paper we propose a generic and simple framework comprising three steps: (i) constructing a cost volume (ii) fast cost volume filtering and (iii) winner-take-all label selection. Our main contribution is to show that with such a simple framework state-of-the-art results can be achieved for several computer vision applications. In particular, we achieve (i) disparity maps in real-time, whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and (ii) optical flow fields with very fine structures as well as large displacements. To demonstrate robustness, the few parameters of our framework are set to nearly identical values for both applications. Also, competitive results for interactive image segmentation are presented. With this work, we hope to inspire other researchers to leverage this framework to other application areas.
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Cites
Fast approximate energy minimization via graph cuts
Boykov, Y., Veksler, O., & Zabih, R. (2001) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.969114 · Cited by 6,988
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
GrabCut: Interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM Transactions on Graphics · doi:10.1145/1015706.1015720 · Cited by 5,703
Self-citationAn experimental comparison of min-cut/max- flow algorithms for energy minimization in vision
Boykov, Y., & Kolmogorov, V. (2004) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2004.60 · Cited by 4,577
PatchMatch: a randomized correspondence algorithm for structural image editing
Barnes, C., Shechtman, E., Finkelstein, A., & Goldman, D. B. (2009) · ACM Transactions on Graphics · doi:10.1145/1531326.1531330 · Cited by 2,880
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
Secrets of optical flow estimation and their principles
Sun, D., Roth, S., & Black, M. J. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539939 · Cited by 1,389
Large Displacement Optical Flow: Descriptor Matching in Variational Motion Estimation
Brox, T., & Malik, J. (2011) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2010.143 · Cited by 1,295
Adaptive support-weight approach for correspondence search
Yoon, K.-J., & Kweon, I. S. (2006) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2006.70 · Cited by 1,194
Sigal, L., Balan, A. O., & Black, M. J. (2010) · International Journal of Computer Vision · doi:10.1007/s11263-009-0273-6 · Cited by 1,164
Cited by
Are we ready for autonomous driving? The KITTI vision benchmark suite
Geiger, A., Lenz, P., & Urtasun, R. (2012) · 2012 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2012.6248074 · Cited by 14,897
He, K., Sun, J., & Tang, X. (2013) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.213 · Cited by 5,546
DTAM: Dense tracking and mapping in real-time
Newcombe, R. A., Lovegrove, S. J., & Davison, A. J. (2011) · 2011 International Conference on Computer Vision · doi:10.1109/iccv.2011.6126513 · Cited by 2,078
Learning to Estimate Hidden Motions with Global Motion Aggregation
Jiang, S., Campbell, D., Lu, Y., Li, H., & Hartley, R. (2021) · 2021 IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv48922.2021.00963 · Cited by 365
Dense Reconstruction Using 3D Object Shape Priors
Dame, A., Prisacariu, V. A., Ren, C. Y., & Reid, I. (2013) · 2013 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2013.170 · Cited by 135
Metadata
- Authors
- Rhemann, C., Hosni, A., Bleyer, M., Rother, C., & Gelautz, M.
- Venue
- CVPR 2011
- Publisher
- IEEE
- Published
- 2011-06
- Pages
- 3017-3024
- OpenAlex
- W1968799614
- Cited by
- 485
- References
- 45
- FWCI
- 41.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Rhemann, C., Hosni, A., Bleyer, M., Rother, C., & Gelautz, M. (2011). Fast cost-volume filtering for visual correspondence and beyond. CVPR 2011. https://doi.org/10.1109/cvpr.2011.5995372
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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