The DOI matches its Crossref record; 26 of the 26 references it cites were found in a registry or an index.
Intel(R) RealSense(TM) Stereoscopic Depth Cameras
Abstract
We present a comprehensive overview of the stereoscopic Intel RealSense RGBD imaging systems. We discuss these systems' mode-of-operation, functional behavior and include models of their expected performance, shortcomings, and limitations. We provide information about the systems' optical characteristics, their correlation algorithms, and how these properties can affect different applications, including 3D reconstruction and gesture recognition. Our discussion covers the Intel RealSense R200 and RS400.
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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
A Stochastic Approximation Method
Robbins, H., & Monro, S. (1951) · The Annals of Mathematical Statistics · doi:10.1214/aoms/1177729586 · Cited by 9,643
Stereo Processing by Semiglobal Matching and Mutual Information
Hirschmuller, H. (2008) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2007.1166 · Cited by 4,195
Non-parametric local transforms for computing visual correspondence
Zabih, R., & Woodfill, J. (1994) · Lecture Notes in Computer Science · doi:10.1007/bfb0028345 · Cited by 1,763
Self-citationHigh-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth
Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-11752-2_3 · Cited by 1,305
Real-time 3D reconstruction at scale using voxel hashing
Nießner, M., Zollhöfer, M., Izadi, S., & Stamminger, M. (2013) · ACM Transactions on Graphics · doi:10.1145/2508363.2508374 · Cited by 1,002
Efficient Large-Scale Stereo Matching
Geiger, A., Roser, M., & Urtasun, R. (2011) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-19315-6_3 · Cited by 761
Evaluation of Stereo Matching Costs on Images with Radiometric Differences
Hirschmuller, H., & Scharstein, D. (2009) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2008.221 · Cited by 721
Decentering Distortion of Lenses
Brown, D. C. (1966) · Photogrammetric Engineering & Remote Sensing · Cited by 718
Domain transform for edge-aware image and video processing
Gastal, E. S. L., & Oliveira, M. M. (2011) · ACM Transactions on Graphics · doi:10.1145/2010324.1964964 · Cited by 620
Domain transform for edge-aware image and video processing
Gastal, E. S. L., & Oliveira, M. M. (2011) · ACM SIGGRAPH 2011 papers · doi:10.1145/1964921.1964964 · Cited by 591
Real-time 3D model acquisition
Rusinkiewicz, S., Hall-Holt, O., & Levoy, M. (2002) · Proceedings of the 29th annual conference on Computer graphics and interactive techniques · doi:10.1145/566570.566600 · Cited by 576
On building an accurate stereo matching system on graphics hardware
Mei, X., Sun, X., Zhou, M., Jiao, S., Wang, H., & Zhang, X. (2011) · 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) · doi:10.1109/iccvw.2011.6130280 · Cited by 551
Cross-Based Local Stereo Matching Using Orthogonal Integral Images
Zhang, K., Lu, J., & Lafruit, G. (2009) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2009.2020478 · Cited by 503
Real-time 3D model acquisition
Rusinkiewicz, S., Hall-Holt, O., & Levoy, M. (2002) · ACM Transactions on Graphics · doi:10.1145/566654.566600 · Cited by 319
A Quantitative Evaluation of Confidence Measures for Stereo Vision
Hu, X., & Mordohai, P. (2012) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/tpami.2012.46 · Cited by 261
Practical Real-Time Imaging Stereo Matcher
Nishihara, H. K. (1984) · Optical Engineering · doi:10.1117/12.7973334 · Cited by 209
Dynamics based 3D skeletal hand tracking
Melax, S., Keselman, L., & Orsten, S. (2013) · Proceedings of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games · doi:10.1145/2448196.2448232 · Cited by 172
Self-citationKonolige, K. (2010) · Proceedings - IEEE International Conference on Robotics and Automation/Proceedings · doi:10.1109/robot.2010.5509796 · Cited by 129
Ensemble Learning for Confidence Measures in Stereo Vision
Haeusler, R., Nair, R. S., & Kondermann, D. (2013) · 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.2013.46 · Cited by 104
HyperDepth: Learning Depth from Structured Light without Matching
Fanello, S., Rhemann, C., Tankovich, V., Kowdle, A., Escolano, S. O., Kim, D., & Izadi, S. (2016) · 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.2016.587 · Cited by 104
3D cost aggregation with multiple minimum spanning trees for stereo matching
Li, L., Yu, X., Zhang, S., Zhao, X., & Zhang, L. (2017) · Applied Optics · doi:10.1364/ao.56.003411 · Cited by 74
Full-Image Guided Filtering for Fast Stereo Matching
Yang, Q., Li, D., Wang, L., et al. (2013) · IEEE Signal Processing Letters · doi:10.1109/lsp.2013.2241759 · Cited by 31
Disparity Estimation by Simultaneous Edge Drawing
Peña, D., & Sutherland, A. (2017) · Lecture notes in computer science · doi:10.1007/978-3-319-54427-4_10 · Cited by 19
REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING
Shahbazi, M., Sohn, G., Théau, J., & Ménard, P. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-3-123-2016 · Cited by 12
Binary confidence evaluation for a stereo vision based depth field processor SoC
Motten, A., Claesen, L., & Pan, Y. (2011) · Asian Conference on Pattern Recognition · doi:10.1109/acpr.2011.6166593 · Cited by 8
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Plizzari, C., Cannici, M., & Matteucci, M. (2021) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2021.103219 · Cited by 409
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Cheng, X., Wang, P., & Yang, R. (2018) · arXiv · doi:10.48550/arxiv.1810.02695 · Cited by 345
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Metadata
- Authors
- Keselman, L., Woodfill, J. I., Grunnet-Jepsen, A., & Bhowmik, A.
- Venue
- 2017 IEEE Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
- Publisher
- IEEE
- Published
- 2017-07
- Pages
- 1267-1276
- OpenAlex
- W2745471877
- Cited by
- 448
- References
- 26
- FWCI
- 9.71
- Type
- conference paper
- Language
- en
- Open access
- closed
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