Skip to main content
SpringerLink
Log in

S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching

  • Conference paper
  • First Online:
Computer Vision – ECCV 2020 (ECCV 2020)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 12348))

Included in the following conference series:

Abstract

Establishing robust and accurate correspondences is a fundamental backbone to many computer vision algorithms. While recent learning-based feature matching methods have shown promising results in providing robust correspondences under challenging conditions, they are often limited in terms of precision. In this paper, we introduce S2DNet, a novel feature matching pipeline, designed and trained to efficiently establish both robust and accurate correspondences. By leveraging a sparse-to-dense matching paradigm, we cast the correspondence learning problem as a supervised classification task to learn to output highly peaked correspondence maps. We show that S2DNet achieves state-of-the-art results on the HPatches benchmark, as well as on several long-term visual localization datasets.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 109.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Phototourism Challenge: CVPR 2019 Image Matching Workshop (2019)

    Google Scholar 

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

    Google Scholar 

  3. Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: Conference on Computer Vision and Pattern Recognition (2012)

    Google Scholar 

  4. Balntas, V., Johns, E., Tang, L., Mikolajczyk, K.: PN-Net: conjoined triple deep network for learning local image descriptors. arXiv Preprint (2016)

    Google Scholar 

  5. Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K.: HPatches: a benchmark and evaluation of handcrafted and learned local descriptors. In: Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  6. Balntas, V., Riba, E., Ponsa, D., Mikolajczyk, K.: Learning local feature descriptors with triplets and shallow convolutional neural networks. In: British Machine Vision Conference (2016)

    Google Scholar 

  7. Bay, H., Tuytelaars, T., Gool, L.V.: SURF: speeded up robust features. In: European Conference on Computer Vision (2006)

    Google Scholar 

  8. Brachmann, E., Rother, C.: Neural-guided RANSAC: learning where to sample model hypotheses. In: ICCV (2019)

    Google Scholar 

  9. Brown, M.A., Hua, G., Winder, S.A.J.: Discriminative learning of local image descriptors. IEEE Trans. Pattern Anal. Mach. Intell. 33, 43–57 (2011)

    Article  Google Scholar 

  10. Calonder, M., Lepetit, V., Strecha, C., Fua, P.: BRIEF: binary robust independent elementary features. In: European Conference on Computer Vision (2010)

    Google Scholar 

  11. Choy, C.B., Gwak, J., Savarese, S., Chandraker, M.: Universal correspondence network. In: Advances in Neural Information Processing Systems, vol. 30 (2016)

    Google Scholar 

  12. Cummins, M.J., Newman, P.: FAB-MAP: probabilistic localization and mapping in the space of appearance. Int. J. Robot. Res. 27, 647–665 (2008)

    Article  Google Scholar 

  13. DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperPoint: self-supervised interest point detection and description. In: CVPR Workshop (2018)

    Google Scholar 

  14. Dong, J., Soatto, S.: Domain-size pooling in local descriptors: DSP-SIFT. In: Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

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

    Google Scholar 

  16. Fischler, M.A., Bolles, R.C.: Random sample consensus: a paradigm for model fitting with applications to image analysis and automated cartography. Commun. ACM 24, 381–395 (1981)

    Article  MathSciNet  Google Scholar 

  17. Gauglitz, S., Höllerer, T., Turk, M.: Evaluation of interest point detectors and feature descriptors for visual tracking. Int. J. Comput. Vision 94, 335 (2011)

    Article  Google Scholar 

  18. Germain, H., Bourmaud, G., Lepetit, V.: Sparse-to-dense hypercolumn matching for long-term visual localization. In: International Conference on 3D Vision (2019)

    Google Scholar 

  19. Hariharan, B., Arbeláez, P.A., Girshick, R.B., Malik, J.: Hypercolumns for object segmentation and fine-grained localization. In: Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  20. Harris, C., Stephens, M.: A combined corner and edge detector. In: Proceedings of Fourth Alvey Vision Conference (1988)

    Google Scholar 

  21. Heinly, J., Schönberger, J.L., Dunn, E., Frahm, J.M.: Reconstructing the world* in six days *(as captured by the Yahoo 100 million image dataset). In: Conference on Computer Vision and Pattern Recognition (2015)

    Google Scholar 

  22. Ioffe, S., Szegedy, C.: Batch normalization: accelerating deep network training by reducing internal covariate shift. arXiv Preprint (2015)

    Google Scholar 

  23. Kim, H., Lee, D., Sim, J., Kim, C.: SOWP: spatially ordered and weighted patch descriptor for visual tracking. In: International Conference on Computer Vision (2015)

    Google Scholar 

  24. Kingma, D.P., Ba, J.: Adam: a method for stochastic optimization. CoRR abs/1412.6980 (2014)

    Google Scholar 

  25. Lebeda, K., Matas, J.E.S., Chum, O.: Fixing the locally optimized RANSAC. In: British Machine Vision Conference (2012)

    Google Scholar 

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

    Google Scholar 

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

    Article  Google Scholar 

  28. Maddern, W., Pascoe, G., Linegar, C., Newman, P.: 1 year, 1000 km: the Oxford RobotCar dataset. Int. J. Robot. Res. 36, 3–15 (2017)

    Article  Google Scholar 

  29. Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. Int. J. Comput. Vision 60, 63–86 (2004)

    Article  Google Scholar 

  30. Mikolajczyk, K., et al.: A comparison of affine region detectors. Int. J. Comput. Vision 65, 43–72 (2005)

    Article  Google Scholar 

  31. Mishchuk, A., Mishkin, D., Radenović, F., Matas, J.: Working hard to know your neighbor’s margins: local descriptor learning loss. In: Advances in Neural Information Processing Systems (2017)

    Google Scholar 

  32. Mishkin, D., Radenović, F., Matas, J.: Repeatability is not enough: learning affine regions via discriminability. In: Ferrari, V., Hebert, M., Sminchisescu, C., Weiss, Y. (eds.) ECCV 2018. LNCS, vol. 11213, pp. 287–304. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-01240-3_18

    Chapter  Google Scholar 

  33. Noh, H., Araujo, A., Sim, J., Weyand, T., Han, B.: Large-scale image retrieval with attentive deep local features. In: International Conference on Computer Vision (2016)

    Google Scholar 

  34. Ono, Y., Trulls, E., Fua, P., Yi, K.M.: LF-Net: learning local features from images. In: NeurIPS (2018)

    Google Scholar 

  35. Perdoch, M., Chum, O., Matas, J.: efficient representation of local geometry for large scale object retrieval. In: Conference on Computer Vision and Pattern Recognition (2009)

    Google Scholar 

  36. Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: Conference on Computer Vision and Pattern Recognition (2007)

    Google Scholar 

  37. Revaud, J., et al.: R2D2: repeatable and reliable detector and descriptor. In: Advances in Neural Information Processing Systems (2019)

    Google Scholar 

  38. Rocco, I., Cimpoi, M., Arandjelović, R., Torii, A., Pajdla, T., Sivic, J.: Neighbourhood consensus networks. In: Advances in Neural Information Processing Systems (2018)

    Google Scholar 

  39. Rublee, E., Rabaud, V., Konolige, K., Bradski, G.R.: ORB: an efficient alternative to SIFT or SURF. In: International Conference on Computer Vision (2011)

    Google Scholar 

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

    Google Scholar 

  41. Sarlin, P.E., DeTone, D., Malisiewicz, T., Rabinovich, A.: SuperGlue: learning feature matching with graph neural networks. In: CVPR (2020). https://arxiv.org/abs/1911.11763

  42. Sattler, T., Havlena, M., Radenovic, F., Schindler, K., Pollefeys, M.: Hyperpoints and fine vocabularies for large-scale location recognition. In: International Conference on Computer Vision (2015)

    Google Scholar 

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

    Article  Google Scholar 

  44. Sattler, T., et al.: Benchmarking 6DOF outdoor visual localization in changing conditions. In: Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

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

    Google Scholar 

  46. Sattler, T., Weyand, T., Leibe, B., Kobbelt, L.: Image retrieval for image-based localization revisited. In: British Machine Vision Conference (2012)

    Google Scholar 

  47. Savinov, N., Seki, A., Ladicky, L., Sattler, T., Pollefeys, M.: Quad-networks: unsupervised learning to rank for interest point detection. In: Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

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

    Google Scholar 

  49. Schönberger, J.L., Zheng, E., Pollefeys, M., Frahm, J.M.: Pixelwise view selection for unstructured multi-view stereo. In: European Conference on Computer Vision (2016)

    Google Scholar 

  50. Simo-Serra, E., Trulls, E., Ferraz, L., Kokkinos, I., Fua, P., Moreno-Noguer, F.: Discriminative learning of deep convolutional feature point descriptors. In: International Conference on Computer Vision (2015)

    Google Scholar 

  51. Simonyan, K., Vedaldi, A., Zisserman, A.: Learning local feature descriptors using convex optimisation. IEEE Trans. Pattern Anal. Mach. Intell. 36, 1573–1585 (2014)

    Article  Google Scholar 

  52. Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. CoRR abs/1409.1556 (2014)

    Google Scholar 

  53. Svärm, L., Enqvist, O., Kahl, F., Oskarsson, M.: City-scale localization for cameras with known vertical direction. IEEE Trans. Pattern Anal. Mach. Intell. 39(7), 1455–1461 (2017)

    Article  Google Scholar 

  54. Svärm, L., Enqvist, O., Oskarsson, M., Kahl, F.: Accurate localization and pose estimation for large 3D models. In: Conference on Computer Vision and Pattern Recognition (2014)

    Google Scholar 

  55. Sweeney, C., Fragoso, V., Höllerer, T., Turk, M.: Large scale SfM with the distributed camera model. In: International Conference on 3D Vision (2016)

    Google Scholar 

  56. Taira, H., et al.: InLoc: indoor visual localization with dense matching and view synthesis. CoRR abs/1803.10368 (2018)

    Google Scholar 

  57. Thomee, B., et al.: YFCC100M: the new data in multimedia research. Commun. ACM 59, 64–73 (2016)

    Article  Google Scholar 

  58. Tian, Y., Fan, B., Wu, F.: L2-Net: deep learning of discriminative patch descriptor in euclidean space. In: Conference on Computer Vision and Pattern Recognition (2017)

    Google Scholar 

  59. Tian, Y., Yu, X., Fan, B., Wu, F., Heijnen, H., Balntas, V.: SOSNet: second order similarity regularization for local descriptor learning. In: Conference on Computer Vision and Pattern Recognition (2019)

    Google Scholar 

  60. Toft, C., et al.: Semantic match consistency for long-term visual localization. In: European Conference on Computer Vision (2018)

    Google Scholar 

  61. Torii, A., Arandjelovic, R., Sivic, J., Okutomi, M., Pajdla, T.: 24/7 place recognition by view synthesis. IEEE Trans. Pattern Anal. Mach. Intell. 40 (2015)

    Google Scholar 

  62. Tuytelaars, T., Mikolajczyk, K.: Local invariant feature detectors: a survey. Found. Trends Comput. Graph. Vision 3 (2007)

    Google Scholar 

  63. Wijmans, E., Furukawa, Y.: Exploiting 2D floorplan for building-scale panorama RGBD alignment. In: Conference on Computer Vision and Pattern Recognition (2016)

    Google Scholar 

  64. Yang, H., Shao, L., Zheng, F., Wang, L., Song, Z.: Recent advances and trends in visual tracking: a review. Neurocomputing 74, 3823–3831 (2011)

    Article  Google Scholar 

  65. Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: LIFT: learned invariant feature transform. In: European Conference on Computer Vision (2016)

    Google Scholar 

  66. Yi, K.M., Trulls, E., Ono, Y., Lepetit, V., Salzmann, M., Fua, P.: Learning to find good correspondences. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)

    Google Scholar 

Download references

Acknowledgement

This project has received funding from the Bosch Research Foundation (Bosch Forschungsstiftung).

Author information

Authors and Affiliations

  1. LIGM, École des Ponts, Univ Gustave Eiffel, CNRS, Marne-la-vallée, France

    Hugo Germain & Vincent Lepetit

  2. Laboratoire IMS, Université de Bordeaux, Bordeaux, France

    Guillaume Bourmaud & Vincent Lepetit

Authors
  1. Hugo Germain
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Guillaume Bourmaud
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Vincent Lepetit
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Hugo Germain .

Editor information

Editors and Affiliations

  1. University of Oxford, Oxford, UK

    Andrea Vedaldi

  2. Graz University of Technology, Graz, Austria

    Horst Bischof

  3. University of Freiburg, Freiburg im Breisgau, Germany

    Thomas Brox

  4. University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Jan-Michael Frahm

1 Electronic supplementary material

Below is the link to the electronic supplementary material.

Supplementary material 1 (pdf 14174 KB)

Rights and permissions

Reprints and permissions

Copyright information

© 2020 Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Germain, H., Bourmaud, G., Lepetit, V. (2020). S2DNet: Learning Image Features for Accurate Sparse-to-Dense Matching. In: Vedaldi, A., Bischof, H., Brox, T., Frahm, JM. (eds) Computer Vision – ECCV 2020. ECCV 2020. Lecture Notes in Computer Science(), vol 12348. Springer, Cham. https://doi.org/10.1007/978-3-030-58580-8_37

Download citation

  • DOI: https://doi.org/10.1007/978-3-030-58580-8_37

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-58579-2

  • Online ISBN: 978-3-030-58580-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation