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High-accuracy stereo depth maps using structured light
Abstract
Progress in stereo algorithm performance is quickly outpacing the ability of existing stereo data sets to discriminate among the best-performing algorithms, motivating the need for more challenging scenes with accurate ground truth information. This paper describes a method for acquiring high-complexity stereo image pairs with pixel-accurate correspondence information using structured light. Unlike traditional range-sensing approaches, our method does not require the calibration of the light sources and yields registered disparity maps between all pairs of cameras and illumination projectors. We present new stereo data sets acquired with our method and demonstrate their suitability for stereo algorithm evaluation. Our results are available at http://www.middlebury.edu/stereo/.
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“Our final type of data consists of modified stereo data. Specifically we include the Teddy dataset in the evaluation set, the ground truth for which was obtained using structured lighting (Scharstein and Szeliski 2003) (Fig. 5).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Scharstein and Szeliski 2003
In the citing paper
“Similarly, for the stereo sequences, pixels where cross-checking failed are excluded (Scharstein and Szeliski 2003).”
From A Database and Evaluation Methodology for Optical Flow (2011) · cites this paper as Scharstein and Szeliski 2003
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Cites
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
Self-citationActive, optical range imaging sensors
Besl, P. J. (1988) · Machine Vision and Applications · doi:10.1007/bf01212277 · Cited by 564
Recent progress in coded structured light as a technique to solve the correspondence problem
Batlle, J., Mouaddib, E. M., & Salví, J. (1998) · Pattern Recognition · doi:10.1016/s0031-3203(97)00074-5 · Cited by 400
Computing rectifying homographies for stereo vision
Loop, C., & Zhang, Z. (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.1999.786928 · Cited by 361
Surfaces from stereo: integrating feature matching, disparity estimation, and contour detection
Hoff, W., & Ahuja, N. (1989) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.16709 · Cited by 299
Spacetime stereo: shape recovery for dynamic scenes
Zhang, L., Curless, B., & Seitz, S. 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.2003.1211492 · Cited by 294
Computer Description of Curved Objects
Agin, & Binford (1976) · IEEE Transactions on Computers · doi:10.1109/tc.1976.1674626 · Cited by 276
Range image acquisition with a single binary-encoded light pattern
Vuylsteke, P., & Oosterlinck, A. (1990) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.44402 · Cited by 244
Better optical triangulation through spacetime analysis
Curless, B., & Levoy, M. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466772 · Cited by 191
Bouguet, J.-Y., & Perona, P. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1998.710699 · Cited by 164
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Metadata
- Authors
- Scharstein, D., & Szeliski, R.
- Affiliations
- Middlebury College (US)
- Microsoft (United States) (US)
- Venue
- 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings.
- Publisher
- IEEE Comput. Soc
- Published
- 2003-11-04
- Pages
- I-195-I-202
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2155479981
- Cited by
- 1,542
- References
- 28
- Citations by year
- 2026: 10 · 2025: 29 · 2024: 31 · 2023: 51 · 2022: 58 · 2021: 80 · 2020: 88 · 2019: 93 · 2018: 93 · 2017: 88 · 2016: 103 · 2015: 110 · 2014: 98 · 2013: 95 · 2012: 105
- FWCI
- 21.66
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings (publishedVersion)
- Topics
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Advanced Vision and Imaging
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Optical measurement and interference techniques
- Physical Sciences › Computer Science › Computer Vision and Pattern Recognition › Satellite Image Processing and Photogrammetry
- Keywords
- stereo vision, structured light, ground truth, disparity map, range sensing
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 ›
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