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.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
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
Self-citationSummed-area tables for texture mapping
Crow, F. C. (1984) · ACM SIGGRAPH Computer Graphics · doi:10.1145/964965.808600 · Cited by 661
Fast cost-volume filtering for visual correspondence and beyond
Rhemann, C., Hosni, A., Bleyer, M., Rother, C., & Gelautz, M. (2011) · CVPR 2011 · doi:10.1109/cvpr.2011.5995372 · Cited by 485
Self-citationA perceptually motivated online benchmark for image matting
Rhemann, C., Rother, C., Wang, J., Gelautz, M., Kohli, P., & Rott, P. (2009) · 2009 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2009.5206503 · Cited by 367
Self-citationStereo Correspondence by Dynamic Programming on a Tree
Veksler, O. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.334 · Cited by 295
GeoS: Geodesic Image Segmentation
Criminisi, A., Sharp, T., & Blake, A. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88682-2_9 · Cited by 277
Multi-resolution real-time stereo on commodity graphics hardware
Yang, R., & Pollefeys, M. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211356 · Cited by 273
Local stereo matching using geodesic support weights
Hosni, A., Bleyer, M., Gelautz, M., & Rhemann, C. (2009) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2009.5414478 · Cited by 233
Self-citationBilateral Filtering-Based Optical Flow Estimation with Occlusion Detection
Xiao, J., Cheng, H., Sawhney, H., Rao, C., & Isnardi, M. (2006) · Lecture Notes in Computer Science · doi:10.1007/11744023_17 · Cited by 229
Real-Time Spatiotemporal Stereo Matching Using the Dual-Cross-Bilateral Grid
Richardt, C., Orr, D., Davies, I., Criminisi, A., & Dodgson, N. A. (2010) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-15558-1_37 · Cited by 223
Jain, A., Thormählen, T., Seidel, H., & Theobalt, C. (2010) · ACM SIGGRAPH Conference and Exhibition on Computer Graphics and Interactive Techniques in Asia (SIGGRAPH Asia) · doi:10.1145/1866158.1866174 · Cited by 219
Motion estimation with non-local total variation regularization
Werlberger, M., Pock, T., & Bischof, H. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539945 · Cited by 216
A Convex Formulation of Continuous Multi-label Problems
Pock, T., Schoenemann, T., Graber, G., Bischof, H., & Cremers, D. (2008) · Lecture Notes in Computer Science · doi:10.1007/978-3-540-88690-7_59 · Cited by 196
Motion detail preserving optical flow estimation
Xu, L., Jia, J.-Y., & Matsushita, Y. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539820 · Cited by 193
Geodesic image and video editing
Criminisi, A., Sharp, T., Rother, C., & P'erez, P. (2010) · ACM Transactions on Graphics · doi:10.1145/1857907.1857910 · Cited by 175
Self-citationLarge displacement optical flow computation withoutwarping
Steinbrücker, F., Pock, T., & Cremers, D. (2009) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2009.5459364 · Cited by 137
De-Maeztu, L., Mattoccia, S., Villanueva, A., & Cabeza, R. (2011) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2011.6126434 · Cited by 102
Vector-valued image regularization with PDE's: a common framework for different applications
Tschumperlé, D., & Rachid Deriche, B. (2003) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2003.1211415 · Cited by 100
Optical flow estimation on coarse-to-fine region-trees using discrete optimization
Lei, C., & Yang, Y.-H. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459253 · Cited by 67
An Experimental Comparison of Discrete and Continuous Shape Optimization Methods
Klodt, M., Schoenemann, T., Kolev, K., Schikora, M., & Cremers, D. (2008) · Lecture notes in computer science · doi:10.1007/978-3-540-88682-2_26 · Cited by 66
Gwosdek, P., Zimmer, H., Grewenig, S., Bruhn, A., & Weickert, J. (2012) · Lecture notes in computer science · doi:10.1007/978-3-642-35740-4_29 · Cited by 39
Optical flow estimation with adaptive convolution kernel prior on discrete framework
Lee, K. J., Kwon, D., Yun, I., & Lee, S.-U. (2010) · 2010 IEEE Computer Society Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2010.5539953 · Cited by 32
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
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
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).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
