nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints

verfasst von : Zixin Luo, Tianwei Shen, Lei Zhou, Siyu Zhu, Runze Zhang, Yao Yao, Tian Fang, Long Quan

Erschienen in: Computer Vision – ECCV 2018

Verlag: Springer International Publishing

Einloggen

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config

KI-gestützte Suche

Aus

Abstract

Learned local descriptors based on Convolutional Neural Networks (CNNs) have achieved significant improvements on patch-based benchmarks, whereas not having demonstrated strong generalization ability on recent benchmarks of image-based 3D reconstruction. In this paper, we mitigate this limitation by proposing a novel local descriptor learning approach that integrates geometry constraints from multi-view reconstructions, which benefits the learning process in terms of data generation, data sampling and loss computation. We refer to the proposed descriptor as GeoDesc, and demonstrate its superior performance on various large-scale benchmarks, and in particular show its great success on challenging reconstruction tasks. Moreover, we provide guidelines towards practical integration of learned descriptors in Structure-from-Motion (SfM) pipelines, showing the good trade-off that GeoDesc delivers to 3D reconstruction tasks between accuracy and efficiency.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 340 Zeitschriften

aus folgenden Fachgebieten:

Bauwesen + Immobilien
Business IT + Informatik
Finance + Banking
Management + Führung
Marketing + Vertrieb
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Jetzt informieren

Vorheriges Kapitel Learning Dynamic Memory Networks for Object Tracking

Nächstes Kapitel Unsupervised Image-to-Image Translation with Stacked Cycle-Consistent Adversarial Networks

Nur mit Berechtigung zugänglich

Abadi, M., et al.: TensorFlow: large-scale machine learning on heterogeneous distributed systems. arXiv (2016)

Arandjelovic, R., Zisserman, A.: Three things everyone should know to improve object retrieval. In: CVPR (2012)

Balntas, V., Lenc, K., Vedaldi, A., Mikolajczyk, K.: HPatches: a benchmark and evaluation of handcrafted and learned local lescriptors. In: CVPR (2017)

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

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

Brown, M.A., Hua, G., Winder, S.A.J.: Discriminative learning of local image descriptors. PAMI 33, 43–57 (2011)CrossRef

Vijay Kumar, B.G., Carneiro, G., Reid, I.: Learning local image descriptors with deep siamese and triplet convolutional networks by minimizing global loss functions. In: CVPR (2016)

Han, X., Leung, T., Jia, Y., Sukthankar, R., Berg, A.C.: MatchNet - unifying feature and metric learning for patch-based matching. In: CVPR (2015)

Heinly, J., Dunn, E., Frahm, J.-M.: Comparative evaluation of binary features. In: Fitzgibbon, A., Lazebnik, S., Perona, P., Sato, Y., Schmid, C. (eds.) ECCV 2012. LNCS, pp. 759–773. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-33709-3_54CrossRef

10.

Kaplan, A., Avraham, T., Lindenbaum, M.: Interpreting the ratio criterion for matching SIFT descriptors. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9909, pp. 697–712. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46454-1_42CrossRef

11.

Labatut, P., Pons, J.P., Keriven, R.: Efficient multi-view reconstruction of large-scale scenes using interest points, delaunay triangulation and graph cuts. In: ICCV (2007)

12.

Li, S., Yuan, L., Sun, J., Quan, L.: Dual-feature warping-based motion model estimation. In: ICCV (2015)

13.

Lin, J., Morere, O., Chandrasekhar, V., Veillard, A., Goh, H.: DeepHash: getting regularization, depth and fine-tuning right. arXiv (2015)

14.

Lowe, D.G.: Distinctive image features from scale-invariant keypoints. In: IJCV (2004)MathSciNetCrossRef

15.

Markuš, N., Pandžić, I.S., Ahlberg, J.: Learning local descriptors by optimizing the keypoint-correspondence criterion. In: ICPR (2016)

16.

Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. PAMI 27, 1615–1630 (2005)CrossRef

17.

Mishchuk, A., Mishkin, D., Radenovic, F.: Working hard to know your neighbor’s margins: local descriptor learning loss. In: NIPS (2017)

18.

Mishkin, D., Matas, J., Perdoch, M., Lenc, K.: WxBS: wide baseline stereo generalizations. In: BMVC (2015)

19.

Mitra, R., et al.: A large dataset for improving patch matching. arXiv (2018)

20.

Morel, J.M., Yu, G.: ASIFT: a new framework for fully affine invariant image comparison. SIAM J. Imaging Sci. 2, 438–469 (2009)MathSciNetCrossRef

21.

Movshovitz-Attias, Y., Toshev, A., Leung, T.K., Ioffe, S., Singh, S.: No Fuss Distance Metric Learning using Proxies. In: ICCV (2017)

22.

Philbin, J., Chum, O., Isard, M., Sivic, J., Zisserman, A.: Object retrieval with large vocabularies and fast spatial matching. In: CVPR (2007)

23.

Radenović, F., Tolias, G., Chum, O.: CNN image retrieval learns from BoW: unsupervised fine-tuning with hard examples. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9905, pp. 3–20. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46448-0_1CrossRef

24.

Schnberger, J.L., Frahm, J.M.: Structure-from-motion revisited. In: CVPR (2016)

25.

Schönberger, J.L., Hardmeier, H., Sattler, T., Pollefeys, M.: Comparative evaluation of hand-crafted and learned local features. In: CVPR (2017)

26.

Shen, T., Zhu, S., Fang, T., Zhang, R., Quan, L.: Graph-based consistent matching for structure-from-motion. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 139–155. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_9CrossRef

27.

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

28.

Sohn, K.: Improved deep metric learning with multi-class n-pair loss objective. In: NIPS (2016)

29.

Tian, B.F.Y., Wu, F: L2-net: deep learning of discriminative patch descriptor in Euclidean space. In: CVPR (2017)

30.

Wilson, K., Snavely, N.: Robust global translations with 1DSfM. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014. LNCS, vol. 8691, pp. 61–75. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10578-9_5CrossRef

31.

Winder, S., Hua, G., Brown, M.: Picking the best daisy. In: CVPR (2009)

32.

Wu, C.: SiftGPU: a GPU implementation of sift (2007). http://cs.unc.edu/~ccwu/siftgpu

33.

Yi, K.M., Trulls, E., Lepetit, V., Fua, P.: LIFT: learned invariant feature transform. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9910, pp. 467–483. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46466-4_28CrossRef

34.

Yi, K.M., Verdie, Y., Fua, P., Lepetit, V.: Learning to assign orientations to feature points. In: CVPR (2015)

35.

Zagoruyko, S., Komodakis, N.: Learning to compare image patches via convolutional neural networks. In: CVPR (2015)

36.

Zhang, R., Li, S., Fang, T., Zhu, S., Quan, L.: Joint camera clustering and surface segmentation for large-scale multi-view stereo. In: ICCV (2015)

37.

Zhang, R., Zhu, S., Fang, T., Quan, L.: Distributed very large scale bundle adjustment by global camera consensus. In: ICCV (2017)

38.

Zhang, X., Yu, F.X., Kumar, S., Chang, S.F.: Learning spread-out local feature descriptors. In: CVPR (2017)

39.

Zhou, L., Zhu, S., Shen, T., Wang, J., Fang, T., Quan, L.: Progressive large scale-invariant image matching in scale space. In: ICCV (2017)

40.

Zhu, S., Fang, T., Xiao, J., Quan, L.: Local readjustment for high-resolution 3D reconstruction. In: CVPR (2014)

41.

Zhu, S., et al.: Very large-scale global SFM by distributed motion averaging. In: CVPR (2018)

Titel: GeoDesc: Learning Local Descriptors by Integrating Geometry Constraints
verfasst von: Zixin Luo
Tianwei Shen
Lei Zhou
Siyu Zhu
Runze Zhang
Yao Yao
Tian Fang
Long Quan
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01239-7

Electronic ISBN: 978-3-030-01240-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01240-3_11

Springer Professional

Abstract

Bitte loggen Sie sich ein, um Zugang zu Ihrer Lizenz zu erhalten.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Springer Professional "Technik"

Springer Professional "Wirtschaft"