The DOI matches its Crossref record; 22 of the 22 references it cites were found in a registry or an index.
Preemptive RANSAC for live structure and motion estimation
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Cites
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Bundle Adjustment — A Modern Synthesis
Triggs, B., McLauchlan, P. F., Hartley, R. I., & Fitzgibbon, A. W. (2000) · Lecture Notes in Computer Science · doi:10.1007/3-540-44480-7_21 · Cited by 3,988
MLESAC: A New Robust Estimator with Application to Estimating Image Geometry
Torr, P. H. S., & Zisserman, A. (2000) · Computer Vision and Image Understanding · doi:10.1006/cviu.1999.0832 · Cited by 2,396
The Development and Comparison of Robust Methods for Estimating the Fundamental Matrix
Torr, P. H. S., & Murray, D. W. (1997) · International Journal of Computer Vision · doi:10.1023/a:1007927408552 · Cited by 755
Pollefeys, M., Koch, R., & Gool, L. V. (1999) · International Journal of Computer Vision · doi:10.1023/a:1008109111715 · Cited by 748
Review and analysis of solutions of the three point perspective pose estimation problem
Haralick, B. M., Lee, C.-N., Ottenberg, K., & Nölle, M. (1994) · International Journal of Computer Vision · doi:10.1007/bf02028352 · Cited by 627
Pollefeys, M., Koch, R., & Van Gool, L. (2002) · Sixth International Conference on Computer Vision (IEEE Cat. No.98CH36271) · doi:10.1109/iccv.1998.710705 · Cited by 577
Randomized RANSAC with T, test
Matas, J., & Chum, O. (2004) · Image and Vision Computing · doi:10.1016/j.imavis.2004.02.009 · Cited by 281
An efficient solution to the five-point relative pose problem
Nister, D. (2003) · 2003 IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 2003. Proceedings. · doi:10.1109/cvpr.2003.1211470 · Cited by 279
Research works printed but not linked: 4
- Zhang, Z.: Determining the epipolar geometry and its uncertainty: a review. Int. J. Comput. Vision 27(2), 161–195 (1998) · DOI
- Hartley, R., Silpa-Anan, C.: Reconstruction from two views using approximate calibration. In: Proceedings of the 5th Asian Conference on Computer Vision, Melbourne, Australia, January 2002
- Björkman, M., Eklundh, J.: Real-time epipolar geometry estimation and disparity. In: Proceedings of the International Conference on Computer Vision, pp. 234–241 (1999) · doi:10.1109/iccv.1999.791225
- Lacey, A., Pinitkarn, N., Thacker, N.: An evaluation of the performance of RANSAC algorithms for stereo camera calibration. In: Proceedings of British Machine Vision Conference, pp. 646–655 (2000) · doi:10.5244/c.14.65
Cited by
Efficient RANSAC for Point‐Cloud Shape Detection
Schnabel, R., Wahl, R., & Klein, R. (2007) · Computer Graphics Forum · doi:10.1111/j.1467-8659.2007.01016.x · Cited by 2,106
Two years of Visual Odometry on the Mars Exploration Rovers
Maimone, M., Cheng, Y., & Matthies, L. (2007) · Journal of Field Robotics · doi:10.1002/rob.20184 · Cited by 655
Visual Odometry : Part II: Matching, Robustness, Optimization, and Applications
Fraundorfer, F., & Scaramuzza, D. (2012) · IEEE Robotics & Automation Magazine · doi:10.1109/mra.2012.2182810 · Cited by 620
DeepIM: Deep Iterative Matching for 6D Pose Estimation
Li, Y., Wang, G., Ji, X., Xiang, Y., & Fox, D. (2018) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-01231-1_42 · Cited by 576
Visual teach and repeat for long‐range rover autonomy
Furgale, P., & Barfoot, T. D. (2010) · Journal of Field Robotics · doi:10.1002/rob.20342 · Cited by 266
Metadata
- Authors
- Nistér, D.
- Venue
- Machine Vision and Applications
- Published
- 2005-11-25
- Volume
- 16
- Issue
- 5
- Pages
- 321-329
- ISSN
- 0932-8092 (ISSN-L)
- 1432-1769
- OpenAlex
- W2134138606
- Cited by
- 255
- References
- 22
- FWCI
- 6.78
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
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- Nistér, D. (2005). Preemptive RANSAC for live structure and motion estimation. Machine Vision and Applications. https://doi.org/10.1007/s00138-005-0006-y
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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.
