The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Worldwide Pose Estimation Using 3D Point Clouds
Open accessAbstract
We address the problem of determining where a photo was taken by estimating a full 6-DOF-plus-intrincs camera pose with respect to a large geo-registered 3D point cloud, bringing together research on image localization, landmark recognition, and 3D pose estimation. Our method scales to datasets with hundreds of thousands of images and tens of millions of 3D points through the use of two new techniques: a co-occurrence prior for RANSAC and bidirectional matching of image features with 3D points. We evaluate our method on several large data sets, and show state-of-the-art results on landmark recognition as well as the ability to locate cameras to within meters, requiring only seconds per query.
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Cites
Distinctive Image Features from Scale-Invariant Keypoints
Lowe, D. G. (2004) · International Journal of Computer Vision · doi:10.1023/b:visi.0000029664.99615.94 · Cited by 55,589
Video Google: a text retrieval approach to object matching in videos
Sivic, & Zisserman (2003) · Proceedings Ninth IEEE International Conference on Computer Vision · doi:10.1109/iccv.2003.1238663 · Cited by 6,367
Scalable Recognition with a Vocabulary Tree
Nister, D., & Stewenius, H. (2006) · 2006 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - Volume 2 (CVPR'06) · doi:10.1109/cvpr.2006.264 · Cited by 3,579
Object retrieval with large vocabularies and fast spatial matching
Philbin, J., Chum, O., Isard, M., Sivic, J., & Zisserman, A. (2007) · 2007 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2007.383172 · Cited by 2,929
Image Alignment and Stitching: A Tutorial
Szeliski, R. (2007) · Foundations and Trends in Computer Graphics and Vision · doi:10.1561/0600000009 · Cited by 1,593
Matching with PROSAC — Progressive Sample Consensus
Chum, O., & Matas, J. (2005) · 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05) · doi:10.1109/cvpr.2005.221 · Cited by 1,096
Agarwal, S., Snavely, N., Simon, I., Seitz, S. M., & Szeliski, R. (2009) · 2009 IEEE 12th International Conference on Computer Vision · doi:10.1109/iccv.2009.5459148 · Cited by 1,067
Self-citationIM2GPS: estimating geographic information from a single image
Hays, J., & Efros, A. A. (2008) · 2008 IEEE Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2008.4587784 · Cited by 877
Crandall, D. J., Backstrom, L., Huttenlocher, D., & Kleinberg, J. (2009) · Proceedings of the 18th international conference on World wide web · doi:10.1145/1526709.1526812 · Cited by 769
Self-citationDeep photo: model-based photograph enhancement and viewing
Kopf, J., Neubert, B., Chen, B., Cohen, M., Cohen-Or, D., Deussen, O., Uyttendaele, M., & Lischinski, D. (2008) · ACM Transactions on Graphics · doi:10.1145/1409060.1409069 · Cited by 713
Cited by
PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization
Kendall, A., Grimes, M., & Cipolla, R. (2015) · 2015 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2015.336 · Cited by 2,489
Geometric Loss Functions for Camera Pose Regression with Deep Learning
Kendall, A., & Cipolla, R. (2017) · 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2017.694 · Cited by 803
Benchmarking 6DOF Outdoor Visual Localization in Changing Conditions
Sattler, T., Maddern, W., Toft, C., Torii, A., Hammarstrand, L., Stenborg, E., Safari, D., Okutomi, M., Pollefeys, M., Sivic, J., et al. (2018) · 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition · doi:10.1109/cvpr.2018.00897 · Cited by 699
Modelling uncertainty in deep learning for camera relocalization
Kendall, A., & Cipolla, R. (2016) · 2016 IEEE International Conference on Robotics and Automation (ICRA) · doi:10.1109/icra.2016.7487679 · Cited by 548
“Visual SLAM [1], [4], [5], [9], and relocalization research [10], [11], has largely focused on registering viewpoints using point-based landmark features, such as SIFT [2] or ORB [3].”
BackgroundImage-Based Localization Using LSTMs for Structured Feature Correlation
Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., & Cremers, D. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.75 · Cited by 521
Metadata
- Authors
- Li, Y., Snavely, N., Huttenlocher, D., & Fua, P.
- Venue
- Lecture Notes in Computer Science
- Publisher
- Springer Berlin Heidelberg
- Published
- 2012
- Pages
- 15-29
- ISSN
- 0302-9743 (ISSN-L)
- 1611-3349
- OpenAlex
- W1616969904
- Cited by
- 366
- References
- 34
- FWCI
- 18.92
- Type
- conference paper
- Language
- en
- Open access
- green
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- Li, Y., Snavely, N., Huttenlocher, D., & Fua, P. (2012). Worldwide Pose Estimation Using 3D Point Clouds. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-642-33718-5_2
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