Skip to main content
Springer Nature Link
Log in

Is Geometry Enough for Matching in Visual Localization?

  • Conference paper
  • First Online:
Computer Vision – ECCV 2022 (ECCV 2022)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 13670))

Included in the following conference series:

Abstract

In this paper, we propose to go beyond the well-established approach to vision-based localization that relies on visual descriptor matching between a query image and a 3D point cloud. While matching keypoints via visual descriptors makes localization highly accurate, it has significant storage demands, raises privacy concerns and requires update to the descriptors in the long-term. To elegantly address those practical challenges for large-scale localization, we present GoMatch, an alternative to visual-based matching that solely relies on geometric information for matching image keypoints to maps, represented as sets of bearing vectors. Our novel bearing vectors representation of 3D points, significantly relieves the cross-modal challenge in geometric-based matching that prevented prior work to tackle localization in a realistic environment. With additional careful architecture design, GoMatch improves over prior geometric-based matching work with a reduction of (\(10.67\,\text {m}, 95.7^{\circ }\)) and (\(1.43\,\text {m}\), \(34.7^{\circ }\)) in average median pose errors on Cambridge Landmarks and 7-Scenes, while requiring as little as \(1.5/1.7\%\) of storage capacity in comparison to the best visual-based matching methods. This confirms its potential and feasibility for real-world localization and opens the door to future efforts in advancing city-scale visual localization methods that do not require storing visual descriptors.

Q. Zhou and S. Agostinho—Equal contribution.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Subscribe and save

Springer+ Basic
$34.99 /Month
  • Get 10 units per month
  • Download Article/Chapter or eBook
  • 1 Unit = 1 Article or 1 Chapter
  • Cancel anytime
Subscribe now

Buy Now

Chapter
USD 29.95
Price excludes VAT (USA)
eBook
USD 89.00
Price excludes VAT (USA)
Softcover Book
USD 119.99
Price excludes VAT (USA)

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    Storage as in non-volatile preservation of data, in contrast to volatile memory.

References

  1. Arandjelovic, R., Gronat, P., Torii, A., Pajdla, T., Sivic, J.: NetVLAD: CNN architecture for weakly supervised place recognition. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  2. Balntas, V., Li, S., Prisacariu, V.: RelocNet: continuous metric learning relocalisation using neural nets. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) Computer Vision – ECCV 2018. LNCS, vol. 11218, pp. 782–799. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01264-9_46

    Chapter  Google Scholar 

  3. Bhowmik, A., Gumhold, S., Rother, C., Brachmann, E.: Reinforced feature points: optimizing feature detection and description for a high-level task. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4948–4957 (2020)

    Google Scholar 

  4. Blanton, H., Greenwell, C., Workman, S., Jacobs, N.: Extending absolute pose regression to multiple scenes. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops (2020)

    Google Scholar 

  5. Brachmann, E., et al.: DSAC - differentiable RANSAC for camera localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  6. Brachmann, E., Rother, C.: Learning less is more - 6D camera localization via 3D surface regression. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  7. Brachmann, E., Rother, C.: Expert sample consensus applied to camera re-localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 7525–7534 (2019)

    Google Scholar 

  8. Brachmann, E., Rother, C.: Neural-guided RANSAC: learning where to sample model hypotheses. In: IEEE International Conference on Computer Vision (ICCV), pp. 4322–4331 (2019)

    Google Scholar 

  9. Brown, M., Windridge, D., Guillemaut, J.-Y.: Globally optimal 2D-3D registration from points or lines without correspondences. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  10. Campbell, D., Liu, L., Gould, S.: Solving the blind perspective-n-point problem end-to-end with robust differentiable geometric optimization. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12347, pp. 244–261. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58536-5_15

    Chapter  Google Scholar 

  11. Campbell, D., Petersson, L., Kneip, L., Li, H.: Globally-optimal inlier set maximisation for simultaneous camera pose and feature correspondence. In: Proceedings of the IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  12. Campbell, D., Petersson, L., Kneip, L., Li, H., Gould, S.: The alignment of the spheres: globally-optimal spherical mixture alignment for camera pose estimation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  13. Camposeco, F., Cohen, A., Pollefeys, M., Sattler, T.: Hybrid scene compression for visual localization. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  14. Cao, S., Snavely, N.: Minimal scene descriptions from structure from motion models. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2014)

    Google Scholar 

  15. Cavallari, T., Bertinetto, L., Mukhoti, J., Torr, P., Golodetz, S.: Let’s take this online: adapting scene coordinate regression network predictions for online RGB-D camera relocalisation. In: 2019 International Conference on 3D Vision (3DV), pp. 564–573 (2019)

    Google Scholar 

  16. Chelani, K., Kahl, F., Sattler, T.: How privacy-preserving are line clouds? Recovering scene details from 3D lines. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 15668–15678 (2021)

    Google Scholar 

  17. Cheng, W., Lin, W., Chen, K., Zhang, X.: Cascaded parallel filtering for memory-efficient image-based localization. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  18. Cuturi, M.: Sinkhorn distances: lightspeed computation of optimal transport. In: Burges, C.J., Bottou, L., Welling, M., Ghahramani, Z., Weinberger, K.Q. (eds.) Advances in Neural Information Processing Systems, vol. 26. Curran Associates Inc. (2013)

    Google Scholar 

  19. David, P., Dementhon, D., Duraiswami, R., Samet, H.: SoftPOSIT: simultaneous pose and correspondence determination. Int. J. Comput. Vis. 59(3), 259–284 (2004)

    Article  MATH  Google Scholar 

  20. DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: CVPR Workshops, pp. 224–236 (2018)

    Google Scholar 

  21. Ding, M., Wang, Z., Sun, J., Shi, J., Luo, P.: CamNet: coarse-to-fine retrieval for camera re-localization. In: IEEE International Conference on Computer Vision (ICCV), pp. 2871–2880 (2019)

    Google Scholar 

  22. Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: Lee, D., Sugiyama, M., Luxburg, U., Guyon, I., Garnett, R. (eds.) Advances in Neural Information Processing Systems, vol. 29. Curran Associates Inc. (2016)

    Google Scholar 

  23. Dosovitskiy, A., Brox, T.: Inverting visual representations with convolutional networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4829–4837 (2016)

    Google Scholar 

  24. Dusmanu, M., Miksik, O., Schonberger, J.L., Pollefeys, M.: Cross-descriptor visual localization and mapping. In: IEEE International Conference on Computer Vision (ICCV), pp. 6058–6067 (2021)

    Google Scholar 

  25. Dusmanu, M., et al.: D2-Net: a trainable CNN for joint detection and description of local features. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  26. Dusmanu, M., Schönberger, J.L., Sinha, S.N., Pollefeys, M.: Privacy-preserving image features via adversarial affine subspace embeddings. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  27. Gao, X.-S., Hou, X.-R., Tang, J., Cheng, H.-F.: Complete solution classification for the perspective-three-point problem. IEEE Trans. Pattern Anal. Mach. Intell. 25(8), 930–943 (2003)

    Article  Google Scholar 

  28. Geppert, M., Larsson, V., Speciale, P., Schönberger, J.L., Pollefeys, M.: Privacy preserving structure-from-motion. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 333–350. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_20

    Chapter  Google Scholar 

  29. Geppert, M., Larsson, V., Speciale, P., Schonberger, J.L., Pollefeys, M.: Privacy preserving localization and mapping from uncalibrated cameras. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1809–1819 (2021)

    Google Scholar 

  30. Germain, H., Bourmaud, G., Lepetit, V.: S2DNet: learning accurate correspondences for sparse-to-dense feature matching. In: European Conference on Computer Vision (ECCV) (2020)

    Google Scholar 

  31. Huang, S., Gojcic, Z., Usvyatsov, M., Wieser, A., Schindler, K.: PREDATOR: registration of 3D point clouds with low overlap. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4267–4276 (2021)

    Google Scholar 

  32. Ke, T., Roumeliotis, S.I.: An efficient algebraic solution to the perspective-three-point problem. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  33. Kendall, A., Cipolla, R.: Modelling uncertainty in deep learning for camera relocalization. In: IEEE International Conference on Robotics and Automation (ICRA) (2016)

    Google Scholar 

  34. Kendall, A., Cipolla, R.: Geometric loss functions for camera pose regression with deep learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  35. Kendall, A., Grimes, M., Cipolla, R.: PoseNet: a convolutional network for real-time 6-DoF camera relocalization. In: IEEE International Conference on Computer Vision (ICCV) (2015)

    Google Scholar 

  36. Kneip, L., Scaramuzza, D., Siegwart, R.: A novel parametrization of the perspective-three-point problem for a direct computation of absolute camera position and orientation. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2011)

    Google Scholar 

  37. Laskar, Z., Melekhov, I., Kalia, S., Kannala, J.: Camera relocalization by computing pairwise relative poses using convolutional neural network. In: IEEE International Conference on Computer Vision (ICCV) Workshops (2017)

    Google Scholar 

  38. Li, X., Wang, S., Zhao, Y., Verbeek, J., Kannala, J.: Hierarchical scene coordinate classification and regression for visual localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 11983–11992 (2020)

    Google Scholar 

  39. Li, Z., Snavely, N.: MegaDepth: learning single-view depth prediction from internet photos. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  40. Liu, L., Campbell, D., Li, H., Zhou, D., Song, X., Yang, R.: Learning 2D-3D correspondences to solve the blind perspective-n-point problem (2020)

    Google Scholar 

  41. Lowe, D.G.: Distinctive image features from scale-invariant keypoints. Int. J. Comput. Vis. 60(2), 91–110 (2004)

    Article  Google Scholar 

  42. Luo, Z., et al.: ASLFeat: learning local features of accurate shape and localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 6589–6598 (2020)

    Google Scholar 

  43. Mera-Trujillo, M., Smith, B., Fragoso, V.: Efficient scene compression for visual-based localization. In: 2020 International Conference on 3D Vision (3DV), pp. 1–10 (2020)

    Google Scholar 

  44. Yi, K.M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., Fua, P.: Learning to find good correspondences. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2666–2674 (2018)

    Google Scholar 

  45. Moreno-Noguer, F., Lepetit, V., Fua, P.: Pose priors for simultaneously solving alignment and correspondence. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5303, pp. 405–418. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88688-4_30

    Chapter  Google Scholar 

  46. Muja, M., Lowe, D.G.: Scalable nearest neighbor algorithms for high dimensional data. IEEE Trans. Pattern Anal. Mach. Intell. 36(11), 2227–2240 (2014)

    Article  Google Scholar 

  47. Ng, T., et al.: NinjaDesc: content-concealing visual descriptors via adversarial learning. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 12797–12807 (2022)

    Google Scholar 

  48. Pittaluga, F., Koppal, S.J., Kang, S.B., Sinha, S.N.: Revealing scenes by inverting structure from motion reconstructions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 145–154 (2019)

    Google Scholar 

  49. Radwan, N., Valada, A., Burgard, W.: VLocNet++: deep multitask learning for semantic visual localization and odometry. IEEE Robot. Autom. Lett. 3(4), 4407–4414 (2018)

    Article  Google Scholar 

  50. Sarlin, P.-E., Cadena, C., Siegwart, R., Dymczyk, M.: From coarse to fine: robust hierarchical localization at large scale. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  51. Sarlin, P.-E., DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperGlue: learning feature matching with graph neural networks. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 4938–4947 (2020)

    Google Scholar 

  52. Sarlin, P.-E., et al.: Back to the feature: learning robust camera localization from pixels to pose. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 3247–3257 (2021)

    Google Scholar 

  53. Sattler, T., Leibe, B., Kobbelt, L.: Efficient & effective prioritized matching for large-scale image-based localization. IEEE Trans. Pattern Anal. Mach. Intell. 39(9), 1744–1756 (2017)

    Article  Google Scholar 

  54. Sattler, T., et al.: Benchmarking 6DoF outdoor visual localization in changing conditions. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8601–8610 (2018)

    Google Scholar 

  55. Sattler, T., et al.: Are large-scale 3D models really necessary for accurate visual localization? In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

    Google Scholar 

  56. Sattler, T., Zhou, Q., Pollefeys, M., Leal-Taixe, L.: Understanding the limitations of CNN-based absolute camera pose regression. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2019)

    Google Scholar 

  57. Schönberger, J.L., Frahm, J.-M.: Structure-from-motion revisited. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2016)

    Google Scholar 

  58. Schönberger, J.L., Pollefeys, M., Geiger, A., Sattler, T.: Semantic visual localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  59. Schönberger, J.L., Zheng, E., Frahm, J.-M., Pollefeys, M.: Pixelwise view selection for unstructured multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 501–518. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_31

    Chapter  Google Scholar 

  60. Shavit, Y., Ferens, R., Keller, Y.: Learning multi-scene absolute pose regression with transformers. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV), pp. 2733–2742 (2021)

    Google Scholar 

  61. Shotton, J., Glocker, B., Zach, C., Izadi, S., Criminisi, A., Fitzgibbon, A.: Scene coordinate regression forests for camera relocalization in RGB-D images. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 2930–2937 (2013)

    Google Scholar 

  62. Sinkhorn, R., Knopp, P.: Concerning nonnegative matrices and doubly stochastic matrices. Pac. J. Math. 21(2), 343–348 (1967)

    Article  MathSciNet  MATH  Google Scholar 

  63. Speciale, P., Schonberger, J.L., Kang, S.B., Sinha, S.N., Pollefeys, M.: Privacy preserving image-based localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 5493–5503 (2019)

    Google Scholar 

  64. Sun, J., Shen, Z., Wang, Y., Bao, H., Zhou, X.: LoFTR: detector-free local feature matching with transformers. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 8922–8931 (2021)

    Google Scholar 

  65. Sun, W., Jiang, W., Trulls, E., Tagliasacchi, A., Yi, K.M.: ACNe: attentive context normalization for robust permutation-equivariant learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2020)

    Google Scholar 

  66. Taira, H., et al.: InLoc: indoor visual localization with dense matching and view synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (2018)

    Google Scholar 

  67. Toft, C., et al.: Long-term visual localization revisited. IEEE Trans. Pattern Anal. Mach. Intell. 44(4), 2074–2088 (2022)

    Article  Google Scholar 

  68. Torii, A., Arandjelovic, R., Sivic, J., Okutomi, M., Pajdla, T.: 24/7 place recognition by view synthesis. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 1808–1817 (2015)

    Google Scholar 

  69. Tran, N.-T., et al.: On-device scalable image-based localization via prioritized cascade search and fast one-many RANSAC. IEEE Trans. Image Process. 28(4), 1675–1690 (2019)

    Article  MathSciNet  Google Scholar 

  70. Vaswani, A., et al.: Attention is all you need. In: Guyon, I., et al. (eds.) Advances in Neural Information Processing Systems, vol. 30. Curran Associates Inc. (2017)

    Google Scholar 

  71. Walch, F., Hazirbas, C., Leal-Taixe, L., Sattler, T., Hilsenbeck, S., Cremers, D.: Image-based localization using LSTMs for structured feature correlation. In: IEEE International Conference on Computer Vision (ICCV) (2017)

    Google Scholar 

  72. Wang, Q., Zhou, X., Hariharan, B., Snavely, N.: Learning feature descriptors using camera pose supervision. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, J.-M. (eds.) ECCV 2020. LNCS, vol. 12346, pp. 757–774. Springer, Cham (2020). https://doi.org/10.1007/978-3-030-58452-8_44

    Chapter  Google Scholar 

  73. Yang, L., Bai, Z., Tang, C., Li, H., Furukawa, Y., Tan, P.: SANet: scene agnostic network for camera localization. In: IEEE Conference on Computer Vision and Pattern Recognition (CVPR), pp. 42–51 (2019)

    Google Scholar 

  74. Zhang, J., et al.: Learning two-view correspondences and geometry using order-aware network. In: Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV) (2019)

    Google Scholar 

  75. Zhou, Q., Sattler, T., Pollefeys, M., Leal-Taixe, L.: To learn or not to learn: visual localization from essential matrices. In: IEEE International Conference on Robotics and Automation (ICRA), pp. 3319–3326. IEEE (2020)

    Google Scholar 

Download references

Acknowledgments

This research was partially funded by the Humboldt Foundation through the Sofja Kovalevskaya Award.

Author information

Authors and Affiliations

  1. Technical University of Munich, Munich, Germany

    Qunjie Zhou, Aljoša Ošep & Laura Leal-Taixé

  2. Universidade de Lisboa, Lisbon, Portugal

    Sérgio Agostinho

Authors
  1. Qunjie Zhou
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Sérgio Agostinho
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Aljoša Ošep
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Laura Leal-Taixé
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Qunjie Zhou .

Editor information

Editors and Affiliations

  1. Tel Aviv University, Tel Aviv, Israel

    Shai Avidan

  2. University College London, London, UK

    Gabriel Brostow

  3. Google AI, Accra, Ghana

    Moustapha Cissé

  4. University of Catania, Catania, Italy

    Giovanni Maria Farinella

  5. Facebook (United States), Menlo Park, CA, USA

    Tal Hassner

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 362 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Zhou, Q., Agostinho, S., Ošep, A., Leal-Taixé, L. (2022). Is Geometry Enough for Matching in Visual Localization?. In: Avidan, S., Brostow, G., Cissé, M., Farinella, G.M., Hassner, T. (eds) Computer Vision – ECCV 2022. ECCV 2022. Lecture Notes in Computer Science, vol 13670. Springer, Cham. https://doi.org/10.1007/978-3-031-20080-9_24

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-20080-9_24

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-20079-3

  • Online ISBN: 978-3-031-20080-9

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation