The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.
From structure-from-motion point clouds to fast location recognition
Abstract
Efficient view registration with respect to a given 3D reconstruction has many applications like inside-out tracking in indoor and outdoor environments, and geo-locating images from large photo collections. We present a fast location recognition technique based on structure from motion point clouds. Vocabulary tree-based indexing of features directly returns relevant fragments of 3D models instead of documents from the images database. Additionally, we propose a compressed 3D scene representation which improves recognition rates while simultaneously reducing the computation time and the memory consumption. The design of our method is based on algorithms that efficiently utilize modern graphics processing units to deliver real-time performance for view registration. We demonstrate the approach by matching hand-held outdoor videos to known 3D urban models, and by registering images from online photo collections to the corresponding landmarks.
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Cites
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Scalable Recognition with a Vocabulary Tree
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A comparison of affine region detectors
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Cited by
Structure-from-Motion Revisited
Schonberger, J. L., & Frahm, J.-M. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.445 · Cited by 6,433
Self-citationFrom Coarse to Fine: Robust Hierarchical Localization at Large Scale
Sarlin, P.-E., Cadena, C., Siegwart, R., & Dymczyk, M. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.01300 · Cited by 1,044
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
Understanding the Limitations of CNN-Based Absolute Camera Pose Regression
Sattler, T., Zhou, Q., Pollefeys, M., & Leal-Taixe, L. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00342 · Cited by 418
Worldwide Pose Estimation Using 3D Point Clouds
Li, Y., Snavely, N., Huttenlocher, D., & Fua, P. (2012) · Lecture Notes in Computer Science · doi:10.1007/978-3-642-33718-5_2 · Cited by 366
Metadata
- Authors
- Irschara, A., Zach, C., Frahm, J.-M., & Bischof, H.
- Venue
- 2009 IEEE Conference on Computer Vision and Pattern Recognition
- Publisher
- IEEE
- Published
- 2009-06
- Pages
- 2599-2606
- ISSN
- 1063-6919 (ISSN-L)
- OpenAlex
- W2125795712
- Cited by
- 494
- References
- 25
- FWCI
- 21.26
- Type
- conference paper
- Language
- en
- Open access
- closed
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- Irschara, A., Zach, C., Frahm, J.-M., & Bischof, H. (2009). From structure-from-motion point clouds to fast location recognition. 2009 IEEE Conference on Computer Vision and Pattern Recognition. https://doi.org/10.1109/cvpr.2009.5206587
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