Abstract
Image-based localization techniques aim to estimate the position and orientation from which a given query images was taken with respect to a 3D model of the scene. Recent advances in Structure-from-Motion, which allow us to reconstruct large scenes in little time, create a need for image-based localization approaches that handle large-scale models consisting of millions of 3D points both efficiently and effectively in order to localize as many query images as possible in as little time as possible. While multiple efficient localization methods based on prioritized feature matching have been proposed recently, they lack the effectiveness of slower approaches. In this chapter, we show that we can increase the effectiveness of approaches based on prioritized 2D-to-3D matching at little to no additional run-time costs by exploiting both spatial and co-visibility relations between the 3D points in the model. The resulting localization framework incorporates both 2D-to-3D and 3D-to-2D matching and achieves state-of-the-art efficiency and effectiveness.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
©Springer-Verlag Berlin Heidelberg 2012.
- 2.
Chapter 11 discusses the method from Li et al. in more detail.
- 3.
Notice that in the case of SfM models, globally similar points cannot be observed together in a single database image since such locally ambiguous structures are removed by applying the ratio test during the pairwise image matching phase of SfM.
- 4.
Source code is available at http://www.graphics.rwth-aachen.de/localization.
- 5.
Remember that matches found for globally repetitive structures are rejected as too ambiguous during 2D-to-3D search and thus also do not trigger Active Search.
References
Agarwal S, Snavely N, Simon I, Seitz S, Szeliski R (2009) Building Rome in a day. In: International conference on computer vision
Cao S, Snavely N (2013) Graph-based discriminative learning for location recognition. In: IEEE conference on computer vision and pattern recognition
Cao S, Snavely N (2014) Minimal scene descriptions from structure from motion models. In: IEEE conference on computer vision and pattern recognition
Castle RO, Klein G, Murray DW (2008) Video-rate localization in multiple maps for wearable augmented reality. In: International symposium on wearable computers
Choudhary S, Narayanan PJ (2012) Visibility probability structure from SfM datasets and applications. In: European conference on computer vision
Chum O, Matas J (2008) Optimal randomized RANSAC. IEEE Trans Pattern Anal Mach Intell 30(8):1472–1482
Chum O, Matas J, Obdržálek S (2004) Enhancing RANSAC by generalized model optimization. In: Asian conference on computer vision
Donoser M, Schmalstieg D (2014) Discriminative feature-to-point matching in image-based locallization. In: IEEE conference on computer vision and pattern recognition
Fischler M, Bolles R (1981) Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun ACM 24(6):381–395
Frahm JM, Fite-Georgel P, Gallup D, Johnson T, Raguram R, Wu C, Jen YH, Dunn E, Clipp B, Lazebnik S, Pollefeys M (2010) Building Rome on a cloudless day. In: ECCV
Hartley RI, Zisserman A (2004) Multiple view geometry in computer vision, 2nd edn. Cambridge University Press
Irschara A, Zach C, Frahm JM, Bischof H (2009) From structure-from-motion point clouds to fast location recognition. In: IEEE conference on computer vision and pattern recognition
Klein G, Murray D (2007) Parallel tracking and mapping for small AR workspaces. In: International symposium on mixed and augmented reality
Lee GH, Fraundorfer F, Pollefeys M (2013) Structureless pose-graph loop-closure with a multi-camera system on a self-driving car. In: Intelligent robots and systems
Li Y, Snavely N, Huttenlocher D, Fua P (2012) Worldwide pose estimation using 3D point clouds. In: European conference on computer vision
Li Y, Snavely N, Huttenlocher DP (2010) Location recognition using prioritized feature matching. In: European conference on computer vision
Lim H, Sinha SN, Cohen MF, Uyttendaele M (2012) Real-time image-based 6-DOF localization in large-scale environments. In: IEEE conference on computer vision and pattern recognition
Lowe D (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vision 60(2):91–110
Middelberg S, Sattler T, Untzelmann O, Kobbelt L (2014) Scalable 6-DOF localization on mobile devices. In: European conference on computer vision, pp 461–468
Muja M, Lowe DG (2009) Fast approximate nearest neighbors with automatic algorithm configuration. In: International conference on computer vision theory and applications
Nister D, Stewenius H (2006) Scalable recognition with a vocabulary tree. In: IEEE conference on computer vision and pattern recognition
Philbin J, Chum O, Isard M, Sivic J, Zisserman A (2008) Lost in quantization: Improving particular object retrieval in large scale image databases. In: IEEE conference on computer vision and pattern recognition (2008)
Sattler T (2013) Efficient & effective image-based localization. Ph.D. thesis, RWTH Aachen University, Aachen, Germany
Sattler T, Leibe B, Kobbelt L (2011) Fast image-based localization using direct 2D-to-3D matching. In: International conference on computer vision
Sattler T, Leibe, B, Kobbelt L (2012) Improving image-based localization by active correspondence search. In: European conference on computer vision
Sattler T, Leibe B, Kobbelt L (2012) Towards fast image-based localization on a city-scale. In: Outdoor and large-scale real-world scene analysis, Lecture Notes in Computer Science, vol 7474. Springer, Berlin, pp 191–211
Sattler T, Weyand T, Leibe B, Kobbelt L (2012) Image retrieval for image-based localization revisited. In: British machine vision conference
Schindler G, Brown M, Szeliski R (2007) City-scale location recognition. In: IEEE Conference on computer vision and pattern recognition
Snavely N, Seitz SM, Szeliski R (2006) Photo tourism: exploring photo collections in 3D. In: ACM SIGGRAPH
Svarm L, Enqvist O, Oskarsson M, Kahl F (2014) Accurate localization and pose estimation for large 3D models. In: IEEE conference on computer vision and pattern recognition (2014)
Zamir AR, Shah M (2010) Accurate image localization based on google maps street view. In: Daniilidis K, Maragos P, Paragios P (eds) ECCV 2010, Part IV. LNCS, vol 6314. Springer, Heidelberg, pp 255–268
Zhang W, Kosecka J (2006) Image based localization in urban environments. In: International symposium on 3D data processing, visualization, and transmission
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2016 Springer International Publishing Switzerland
About this chapter
Cite this chapter
Sattler, T., Leibe, B., Kobbelt, L. (2016). Exploiting Spatial and Co-visibility Relations for Image-Based Localization. In: Zamir, A., Hakeem, A., Van Gool, L., Shah, M., Szeliski, R. (eds) Large-Scale Visual Geo-Localization. Advances in Computer Vision and Pattern Recognition. Springer, Cham. https://doi.org/10.1007/978-3-319-25781-5_9
Download citation
DOI: https://doi.org/10.1007/978-3-319-25781-5_9
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-25779-2
Online ISBN: 978-3-319-25781-5
eBook Packages: Computer ScienceComputer Science (R0)