The DOI matches its Crossref record; 31 of the 31 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: 1) constructing a cost volume, 2) fast cost volume filtering, and 3) Winner-Takes-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 1) disparity maps in real time whose quality exceeds those of all other fast (local) approaches on the Middlebury stereo benchmark, and 2) optical flow fields which contain 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
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
"GrabCut": interactive foreground extraction using iterated graph cuts
Rother, C., Kolmogorov, V., & Blake, A. (2004) · ACM SIGGRAPH 2004 Papers · doi:10.1145/1186562.1015720 · Cited by 935
Self-citationPatchMatch Stereo - Stereo Matching with Slanted Support Windows
Bleyer, M., Rhemann, C., & Rother, C. (2011) · Procedings of the British Machine Vision Conference 2011 · doi:10.5244/c.25.14 · Cited by 736
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Cited by
PWC-Net: CNNs for Optical Flow Using Pyramid, Warping, and Cost Volume
Sun, D., Yang, X., Liu, M.-Y., & Kautz, J. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00931 · Cited by 2,648
Spectral–Spatial Hyperspectral Image Classification With Edge-Preserving Filtering
Kang, X., Li, S., & Benediktsson, J. A. (2014) · IEEE Transactions on Geoscience and Remote Sensing · doi:10.1109/tgrs.2013.2264508 · Cited by 704
Optical flow modeling and computation: A survey
Fortun, D., Bouthemy, P., & Kervrann, C. (2015) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2015.02.008 · Cited by 431
StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction
Khamis, S., Fanello, S., Rhemann, C., Kowdle, A., Valentin, J., & Izadi, S. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01267-0_35 · Cited by 401
Self-citationLearning 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
Metadata
- Authors
- Hosni, A., Rhemann, C., Bleyer, M., Rother, C., & Gelautz, M.
- Venue
- IEEE Transactions on Pattern Analysis and Machine Intelligence
- Published
- 2013-02
- Volume
- 35
- Issue
- 2
- Pages
- 504-511
- ISSN
- 0162-8828 (ISSN-L)
- 2160-9292
- OpenAlex
- W2148534289
- Cited by
- 673
- References
- 31
- FWCI
- 39.01
- Type
- journal article
- Language
- en
- Open access
- closed
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