nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

MVSNet: Depth Inference for Unstructured Multi-view Stereo

verfasst von : Yao Yao, Zixin Luo, Shiwei Li, 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

We present an end-to-end deep learning architecture for depth map inference from multi-view images. In the network, we first extract deep visual image features, and then build the 3D cost volume upon the reference camera frustum via the differentiable homography warping. Next, we apply 3D convolutions to regularize and regress the initial depth map, which is then refined with the reference image to generate the final output. Our framework flexibly adapts arbitrary N-view inputs using a variance-based cost metric that maps multiple features into one cost feature. The proposed MVSNet is demonstrated on the large-scale indoor DTU dataset. With simple post-processing, our method not only significantly outperforms previous state-of-the-arts, but also is several times faster in runtime. We also evaluate MVSNet on the complex outdoor Tanks and Temples dataset, where our method ranks first before April 18, 2018 without any fine-tuning, showing the strong generalization ability of MVSNet.

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 PlaneMatch: Patch Coplanarity Prediction for Robust RGB-D Reconstruction

Nächstes Kapitel ActiveStereoNet: End-to-End Self-supervised Learning for Active Stereo Systems

Nur mit Berechtigung zugänglich

Validation set: scans \(\{\)3, 5, 17, 21, 28, 35, 37, 38, 40, 43, 56, 59, 66, 67, 82, 86, 106, 117\(\}\). Evaluation set: scans \(\{\)1, 4, 9, 10, 11, 12, 13, 15, 23, 24, 29, 32, 33, 34, 48, 49, 62, 75, 77, 110, 114, 118\(\}\). Training set: the other 79 scans.

Aanæs, H., Jensen, R.R., Vogiatzis, G., Tola, E., Dahl, A.B.: Large-scale data for multiple-view stereopsis. Int. J. Comput. Vis. (IJCV) 120, 153–168 (2016)MathSciNetCrossRef

Abadi, M., et al.: TensorFlow: Large-scale machine learning on heterogeneous systems (2015). https://www.tensorflow.org/

Campbell, N.D.F., Vogiatzis, G., Hernández, C., Cipolla, R.: Using multiple hypotheses to improve depth-maps for multi-view stereo. In: Forsyth, D., Torr, P., Zisserman, A. (eds.) ECCV 2008. LNCS, vol. 5302, pp. 766–779. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-88682-2_58CrossRef

Chen, L.C., Papandreou, G., Kokkinos, I., Murphy, K., Yuille, A.L.: DeepLab: semantic image segmentation with deep convolutional nets, atrous convolution, and fully connected CRFs. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 40, 834–848 (2017)CrossRef

Collins, R.T.: A space-sweep approach to true multi-image matching. In: Computer Vision and Pattern Recognition (CVPR) (1996)

Fuhrmann, S., Langguth, F., Goesele, M.: MVE-a multi-view reconstruction environment. In: Eurographics Workshop on Graphics and Cultural Heritage (GCH) (2014)

Furukawa, Y., Ponce, J.: Accurate, dense, and robust multiview stereopsis. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 32, 1362–1376 (2010)CrossRef

Galliani, S., Lasinger, K., Schindler, K.: Massively parallel multiview stereopsis by surface normal diffusion. In: International Conference on Computer Vision (ICCV) (2015)

Geiger, A., Lenz, P., Urtasun, R.: Are we ready for autonomous driving? the KITTI vision benchmark suite. In: Computer Vision and Pattern Recognition (CVPR) (2012)

10.

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

11.

Hartmann, W., Galliani, S., Havlena, M., Van Gool, L., Schindler, K.: Learned multi-patch similarity. In: International Conference on Computer Vision (ICCV) (2017)

12.

Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 30, 328–341 (2008)CrossRef

13.

Hirschmuller, H., Scharstein, D.: Evaluation of cost functions for stereo matching. In: Computer Vision and Pattern Recognition (CVPR) (2007)

14.

Ji, M., Gall, J., Zheng, H., Liu, Y., Fang, L.: SurfaceNet: an end-to-end 3D neural network for multiview stereopsis. In: International Conference on Computer Vision (ICCV) (2017)

15.

Kar, A., Häne, C., Malik, J.: Learning a multi-view stereo machine. In: Advances in Neural Information Processing Systems (NIPS) (2017)

16.

Kazhdan, M., Hoppe, H.: Screened poisson surface reconstruction. ACM Trans. Graph. (TOG) 32, 29 (2013)CrossRef

17.

Kendall, A., Martirosyan, H., Dasgupta, S., Henry, P.: End-to-end learning of geometry and context for deep stereo regression. In: Computer Vision and Pattern Recognition (CVPR) (2017)

18.

Knapitsch, A., Park, J., Zhou, Q.Y., Koltun, V.: Tanks and temples: benchmarking large-scale scene reconstruction. ACM Trans. Graph. (TOG) 36, 78 (2017)CrossRef

19.

Knöbelreiter, P., Reinbacher, C., Shekhovtsov, A., Pock, T.: End-to-end training of hybrid CNN-CRF models for stereo. In: Computer Vision and Pattern Recognition (CVPR) (2017)

20.

Kutulakos, K.N., Seitz, S.M.: A theory of shape by space carving. Int. J. Comput. Vis. (IJCV) 38, 199–218 (2000)CrossRef

21.

Langguth, F., Sunkavalli, K., Hadap, S., Goesele, M.: Shading-aware multi-view stereo. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 469–485. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_29CrossRef

22.

Lhuillier, M., Quan, L.: A quasi-dense approach to surface reconstruction from uncalibrated images. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 27, 418–433 (2005)CrossRef

23.

Luo, W., Schwing, A.G., Urtasun, R.: Efficient deep learning for stereo matching. In: Computer Vision and Pattern Recognition (CVPR) (2016)

24.

Mayer, N., et al.: A large dataset to train convolutional networks for disparity, optical flow, and scene flow estimation. In: Computer Vision and Pattern Recognition (CVPR) (2016)

25.

Menze, M., Geiger, A.: Object scene flow for autonomous vehicles. In: Computer Vision and Pattern Recognition (CVPR) (2015)

26.

Merrell, P., et al.: Real-time visibility-based fusion of depth maps. In: International Conference on Computer Vision (ICCV) (2007)

27.

Moulon, P., Monasse, P., Marlet, R., et al.: OpenMVG: an open multiple view geometry library. https://github.com/openMVG/openMVG

28.

Newcombe, R.A., et al.: KinectFusion: real-time dense surface mapping and tracking. In: IEEE International Symposium on Mixed and Augmented Reality (ISMAR) (2011)

29.

OpenMVS: open multi-view stereo reconstruction library. https://github.com/cdcseacave/openMVS

30.

Pix4D: https://pix4d.com/

31.

Ronneberger, O., Fischer, P., Brox, T.: U-Net: convolutional networks for biomedical image segmentation. In: Navab, N., Hornegger, J., Wells, W.M., Frangi, A.F. (eds.) MICCAI 2015. LNCS, vol. 9351, pp. 234–241. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24574-4_28CrossRef

32.

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_31CrossRef

33.

Seitz, S.M., Dyer, C.R.: Photorealistic scene reconstruction by voxel coloring. Int. J. Comput. Vis. (IJCV) 35, 151–173 (1999)CrossRef

34.

Seki, A., Pollefeys, M.: SGM-Nets: semi-global matching with neural networks. In: Computer Vision and Pattern Recognition Workshops (CVPRW) (2017)

35.

Tola, E., Strecha, C., Fua, P.: Efficient large-scale multi-view stereo for ultra high-resolution image sets. In: Machine Vision and Applications (MVA) (2012)

36.

Vu, H.H., Labatut, P., Pons, J.P., Keriven, R.: High accuracy and visibility-consistent dense multiview stereo. IEEE Trans. Pattern Anal. Mach. Intell. (TPAMI) 34, 889–901 (2012)CrossRef

37.

Xu, N., Price, B., Cohen, S., Huang, T.: Deep image matting. In: Computer Vision and Pattern Recognition (CVPR) (2017)

38.

Yao, Y., Li, S., Zhu, S., Deng, H., Fang, T., Quan, L.: Relative camera refinement for accurate dense reconstruction. In: 3D Vision (3DV) (2017)

39.

Zbontar, J., LeCun, Y.: Stereo matching by training a convolutional neural network to compare image patches. J. Mach. Learn. Res. (JMLR) 17, 2 (2016)MATH

40.

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

Titel: MVSNet: Depth Inference for Unstructured Multi-view Stereo
verfasst von: Yao Yao
Zixin Luo
Shiwei Li
Tian Fang
Long Quan
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01236-6

Electronic ISBN: 978-3-030-01237-3

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01237-3_47

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"

Premium Partner