nach oben

Erschienen in:

2018 | OriginalPaper | Buchkapitel

Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility

verfasst von : Benjamin Hepp, Debadeepta Dey, Sudipta N. Sinha, Ashish Kapoor, Neel Joshi, Otmar Hilliges

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

Camera equipped drones are nowadays being used to explore large scenes and reconstruct detailed 3D maps. When free space in the scene is approximately known, an offline planner can generate optimal plans to efficiently explore the scene. However, for exploring unknown scenes, the planner must predict and maximize usefulness of where to go on the fly. Traditionally, this has been achieved using handcrafted utility functions. We propose to learn a better utility function that predicts the usefulness of future viewpoints. Our learned utility function is based on a 3D convolutional neural network. This network takes as input a novel volumetric scene representation that implicitly captures previously visited viewpoints and generalizes to new scenes. We evaluate our method on several large 3D models of urban scenes using simulated depth cameras. We show that our method outperforms existing utility measures in terms of reconstruction performance and is robust to sensor noise.

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 SketchyScene: Richly-Annotated Scene Sketches

Nächstes Kapitel Revisiting RCNN: On Awakening the Classification Power of Faster RCNN

Nur mit Berechtigung zugänglich

https://matterport.com/.

Armeni, I., Sax, S., Zamir, A.R., Savarese, S.: Joint 2d–3d-semantic data for indoor scene understanding, Preprint arXiv:1702.01105 (2017)

Bircher, A., Kamel, M., Alexis, K., Oleynikova, H., Siegwart, R.: Receding horizon“next-best-view" planner for 3d exploration. In: 2016 IEEE International Conference on Robotics and Automation (ICRA), pp. 1462–1468. IEEE (2016)

Chen, S., Li, Y., Kwok, N.M.: Active vision in robotic systems: a survey of recent developments. Int. J. Robot. Res. 30(11), 1343–1377 (2011)CrossRef

Choudhury, S., Kapoor, A., Ranade, G., Scherer, S., Dey, D.: Adaptive information gathering via imitation learning. Robotics Science and Systems (2017)

Choy, C.B., Xu, D., Gwak, J., Chen, K., Savarese, S.: 3D-R2N2: a unified approach for single and multi-view 3d object reconstruction. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9912, pp. 628–644. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46484-8_38CrossRef

Dai, A., Chang, A.X., Savva, M., Halber, M., Funkhouser, T., Nießner, M.: Scannet: richly-annotated 3d reconstructions of indoor scenes. http://arxiv.org/abs/1702.04405 (2017)

Dai, A., Qi, C.R., Nießner, M.: Shape completion using 3d-encoder-predictor cnns and shape synthesis. http://arxiv.org/abs/1612.00101 (2016)

Delmerico, J., Isler, S., Sabzevari, R., Scaramuzza, D.: A comparison of volumetric information gain metrics for active 3d object reconstruction. Autonomous Robots pp. 1–12 (2017)

Devrim Kaba, M., Gokhan Uzunbas, M., Nam Lim, S.: A reinforcement learning approach to the view planning problem. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 6933–6941 (2017)

10.

Dunn, E., Frahm, J.M.: Next best view planning for active model improvement. In: BMVC, pp. 1–11 (2009)

11.

Feige, U.: A threshold of ln n for approximating set cover. JACM (1998)

12.

Forster, C., Pizzoli, M., Scaramuzza, D.: Appearance-based active, monocular, dense reconstruction for micro aerial vehicles. In: Robotics: Science and Systems (RSS) (2014)

13.

Fraundorfer, F., Heng, L., Honegger, D., Lee, G.H., Meier, L., Tanskanen, P., Pollefeys, M.: Vision-based autonomous mapping and exploration using a quadrotor mav. In: 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), pp. 4557–4564. IEEE (2012)

14.

Ge, L., Liang, H., Yuan, J., Thalmann, D.: 3d convolutional neural networks for efficient and robust hand pose estimation from single depth images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 1991–2000 (2017)

15.

Glorot, X., Bengio, Y.: Understanding the difficulty of training deep feedforward neural networks. In: Proceedings of the Thirteenth International Conference on Artificial Intelligence and Statistics, pp. 249–256 (2010)

16.

Golovin, D., Krause, A.: Adaptive submodularity: Theory and applications in active learning and stochastic optimization. JAIR (2011). https://arxiv.org/pdf/1003.3967v4.pdf

17.

He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learning for image recognition. In: The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) (June 2016)

18.

He, K., Zhang, X., Ren, S., Sun, J.: Identity mappings in deep residual networks. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9908, pp. 630–645. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46493-0_38CrossRef

19.

Heng, L., Gotovos, A., Krause, A., Pollefeys, M.: Efficient visual exploration and coverage with a micro aerial vehicle in unknown environments. In: ICRA (2015). http://ieeexplore.ieee.org/document/7139309/

20.

Hepp, B., Nießner, M., Hilliges, O.: Plan3d: Viewpoint and trajectory optimization for aerial multi-view stereo reconstruction, Preprint arXiv:1705.09314 (2017)

21.

Hirschmuller, H.: Stereo processing by semiglobal matching and mutual information. IEEE Trans. Pattern Anal. Mach. Intell. 30(2), 328–341 (2008)CrossRef

22.

Hollinger, G.A., Englot, B., Hover, F.S., Mitra, U., Sukhatme, G.S.: Active planning for underwater inspection and the benefit of adaptivity. IJRR (2012). http://journals.sagepub.com/doi/abs/10.1177/0278364912467485

23.

Hornung, A., Wurm, K.M., Bennewitz, M., Stachniss, C., Burgard, W.: OctoMap: an efficient probabilistic 3D mapping framework based on octrees. Autonomous Robots (2013). 10.1007/s10514-012-9321-0, software available at http://octomap.github.com

24.

Isler, S., Sabzevari, R., Delmerico, J., Scaramuzza, D.: An Information Gain Formulation for Active Volumetric 3D Reconstruction. In: ICRA (2016). http://ieeexplore.ieee.org/document/7487527/

25.

Kingma, D., Ba, J.: Adam: a method for stochastic optimization, Preprint arXiv:1412.6980 (2014)

26.

Krause, A., Golovin, D.: Submodular function maximization. In: Tractability: Practical Approaches to Hard Problems (2012). https://las.inf.ethz.ch/files/krause12survey.pdf

27.

Kriegel, S., Rink, C., Bodenmüller, T., Suppa, M.: Efficient next-best-scan planning for autonomous 3d surface reconstruction of unknown objects. J. Real-Time Image Proces. 10(4), 611–631 (2015)CrossRef

28.

Liu, F., Shen, C., Lin, G.: Deep convolutional neural fields for depth estimation from a single image. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 5162–5170 (2015)

29.

Nemhauser, G.L., Wolsey, L.A., Fisher, M.L.: An analysis of approximations for maximizing submodular set functionsi. Math. Program. 14(1), 265–294 (1978)CrossRef

30.

Riegler, G., Ulusoy, A.O., Geiger, A.: Octnet: Learning deep 3d representations at high resolutions. In: Conference on Computer Vision and Pattern Recognition (CVPR) (2017)

31.

Roberts, M., Dey, D., Truong, A., Sinha, S., Shah, S., Kapoor, A., Hanrahan, P., Joshi, N.: Submodular trajectory optimization for aerial 3d scanning. In: International Conference on Computer Vision (ICCV) (2017)

32.

Shen, S., Michael, N., Kumar, V.: Autonomous multi-floor indoor navigation with a computationally constrained mav. In: 2011 IEEE International Conference on Robotics and Automation (ICRA), pp. 20–25. IEEE (2011)

33.

Song, S., Xiao, J.: Deep sliding shapes for amodal 3d object detection in rgb-d images. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 808–816 (2016)

34.

Thrun, S., Burgard, W., Fox, D.: Probabilistic Robotics. MIT Press, Cambridge (2005)MATH

35.

Vasquez-Gomez, J.I., Sucar, L.E., Murrieta-Cid, R., Lopez-Damian, E.: Volumetric next-best-view planning for 3d object reconstruction with positioning error. Int. J. Adv. Robot. Syst. 11(10), 159 (2014)CrossRef

36.

Wenhardt, S., Deutsch, B., Angelopoulou, E., Niemann, H.: Active visual object reconstruction using d-, e-, and t-optimal next best views. In: 2007 IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–7. IEEE (2007)

37.

Xu, K., Zheng, L., Yan, Z., Yan, G., Zhang, E., Nießner, M., Deussen, O., Cohen-Or, D., Huang, H.: Autonomous reconstruction of unknown indoor scenes guided by time-varying tensor fields. ACM Trans. Gr. (TOG) 36, 202 (2017)

38.

Yamauchi, B.: A frontier-based approach for autonomous exploration. In: 1997 IEEE International Symposium on Computational Intelligence in Robotics and Automation, CIRA’97, pp. 146–151. IEEE (1997)

39.

Zamir, A.R., Wekel, T., Agrawal, P., Wei, C., Malik, Jitendra, Savarese, Silvio: Generic 3D representation via pose estimation and matching. In: Leibe, B., Matas, J., Sebe, N., Welling, M. (eds.) ECCV 2016. LNCS, vol. 9907, pp. 535–553. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46487-9_33CrossRef

40.

Zeng, A., Song, S., Nießner, M., Fisher, M., Xiao, J., Funkhouser, T.: 3dmatch: Learning local geometric descriptors from rgb-d reconstructions. In: CVPR (2017)

Titel: Learn-to-Score: Efficient 3D Scene Exploration by Predicting View Utility
verfasst von: Benjamin Hepp
Debadeepta Dey
Sudipta N. Sinha
Ashish Kapoor
Neel Joshi
Otmar Hilliges
Verlag: Springer International Publishing
Buch: Computer Vision – ECCV 2018
Print ISBN: 978-3-030-01266-3

Electronic ISBN: 978-3-030-01267-0

Copyright-Jahr: 2018
DOI: https://doi.org/10.1007/978-3-030-01267-0_27

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"