The DOI matches its Crossref record; 26 of the 28 references it cites were found in a registry or an index.
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
Environment matting extensions
Chuang, Y., Zongker, D. E., Hindorff, J., Curless, B., Salesin, D., & Szeliski, R. (2000) · ACM SIGGRAPH Conference (SIGGRAPH) · doi:10.1145/344779.344844 · Cited by 135
Self-citationSpacetime stereo: a unifying framework for depth from triangulation
Davis, J., Ramamoorthi, R., & Rusinkiewicz, 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.2003.1211491 · Cited by 124
Toward optimal structured light patterns
Horn, E., & Kiryati, N. (1999) · Image and Vision Computing · doi:10.1016/s0262-8856(98)00113-9 · Cited by 124
Three-dimensional surface measurement by space encoding range imaging
Sato, K., & Inokuchi, S. (1985) · Journal of Robotic Systems · Cited by 119
Range data acquisition using color structured lighting and stereo vision
Chen, C., Hung, Y., Chiang, C.-C., & Wu, J. (1997) · Image and Vision Computing · doi:10.1016/s0262-8856(96)01148-1 · Cited by 106
Occlusion detectable stereo-occlusion patterns in camera matrix
Nakamura, Y., Matsuura, T., Satoh, K., & Ohta, Y. (1996) · 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.1996.517099 · Cited by 106
A multibaseline stereo system with active illumination and real-time image acquisition
Kang, S. B., WEBB, J. A. C., Zitnick, C. L., & Takeo Kanade, A. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1995.466802 · Cited by 89
Active Optical Range Imaging Sensors
Besl, P. J. (1989) · Springer series in perception engineering · doi:10.1007/978-1-4612-4532-2_1 · Cited by 78
Helmholtz Stereopsis: Exploiting Reciprocity for Surface Reconstruction
Zickler, T. E., Belhumeur, P. N., & Kriegman, D. J. (2002) · Lecture notes in computer science · doi:10.1007/3-540-47977-5_57 · Cited by 62
Acquisition and visualization of colored 3D objects
Pulli, K., Abi-Rached, H., Duchamp, T., Shapiro, L. G., & Stuetzle, W. (2002) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1998.711067 · Cited by 49
Parallel three-dimensional sensing by color-coded triangulation
Häusler, G., & Ritter, D. (1993) · Applied Optics · doi:10.1364/ao.32.007164 · Cited by 43
Performance evaluation of stereo for tele-presence
Mulligan, J., Isler, V., & Daniilidis, K. (2002) · IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2001.937675 · Cited by 41
Regularizing phase-based stereo
Fröhlinghaus, T., & Buhmann, J. M. (1996) · Proceedings - International Conference on Pattern Recognition/Proceedings/International Conference on Pattern Recognition · doi:10.1109/icpr.1996.546067 · Cited by 40
Toward optimal structured light patterns
Horn, E., & Kiryati, N. (2002) · International Conference on 3-D Digital Imaging and Modeling (3DIM) · doi:10.1109/im.1997.603845 · Cited by 24
Reading between the lines-a method for extracting dynamic 3D with texture
Proesmans, M., Gool, L. V., & Defoort, F. (2002) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.1998.710851 · Cited by 16
Fast 3D object recognition using multiple color coded illumination
Schubert, E. (2002) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.1997.595437 · Cited by 9
Research works printed but not linked: 1
- golub, 1996, Matrix Computation
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Stereo Processing by Semiglobal Matching and Mutual Information
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DehazeNet: An End-to-End System for Single Image Haze Removal
Cai, B., Xu, X., Jia, K., Qing, C., & Tao, D. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2598681 · Cited by 3,367
A Fast Single Image Haze Removal Algorithm Using Color Attenuation Prior
Zhu, Q., Mai, J., & Shao, L. (2015) · IEEE Transactions on Image Processing · doi:10.1109/tip.2015.2446191 · Cited by 2,484
AOD-Net: All-in-One Dehazing Network
Li, B., Peng, X., Wang, Z., Xu, J., & Feng, D. (2017) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.511 · Cited by 2,658
Benchmarking Single-Image Dehazing and Beyond
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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
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