Skip to main content
Fachgebiete
Automobil + Motoren Bauwesen + Immobilien Business IT + Informatik Elektrotechnik + Elektronik Energie + Nachhaltigkeit Finance + Banking Management + Führung Marketing + Vertrieb Maschinenbau + Werkstoffe Versicherung + Risiko
DE
EN
Bücher
Zeitschriften
Themenseiten
Organisationspsychologie Projektmanagement Marketing Smart Manufacturing
Weitere Formate
Podcasts Webinare Technik Webinare Wirtschaft Kongresse Veranstaltungskalender Awards
Jetzt Einzelzugang starten
Zugang für Unternehmen
Referenzkunden
SLX-Digitalkonferenz: Zukunftswerkstatt 2024

Mehr Umsatz mit Top-Kontakten

Am 7. Mai erfahren Sie, wie Vertriebsteams Leadgenerierung, Leadnurturing und Leadstrategien effizient steuern können.
Erweiterte Suche
Anmelden
nach oben
Erschienen in:
CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples Kapitel lesen Erstes Kapitel lesen

2016 | OriginalPaper | Buchkapitel

SDF-2-SDF: Highly Accurate 3D Object Reconstruction

verfasst von : Miroslava Slavcheva, Wadim Kehl, Nassir Navab, Slobodan Ilic

Erschienen in: Computer Vision – ECCV 2016

Verlag: Springer International Publishing

Einloggen

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

search-config
loading …

Abstract

This paper addresses the problem of 3D object reconstruction using RGB-D sensors. Our main contribution is a novel implicit-to-implicit surface registration scheme between signed distance fields (SDFs), utilized both for the real-time frame-to-frame camera tracking and for the subsequent global optimization. SDF-2-SDF registration circumvents expensive correspondence search and allows for incorporation of multiple geometric constraints without any dependence on texture, yielding highly accurate 3D models. An extensive quantitative evaluation on real and synthetic data demonstrates improved tracking and higher fidelity reconstructions than a variety of state-of-the-art methods. We make our data publicly available, creating the first object reconstruction dataset to include ground-truth CAD models and RGB-D sequences from sensors of various quality.

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 Photo Aesthetics Ranking Network with Attributes and Content Adaptation
Nächstes Kapitel Knowledge Transfer for Scene-Specific Motion Prediction
Anhänge
Nur mit Berechtigung zugänglich
Fußnoten
1
The bunny is from The Stanford Repository [35], all other models are freely available at http://​archive3d.​net/​. Object sizes are listed in the supplementary material.
 
Literatur
1.
Besl, P.J., McKay, N.D.: A method for registration of 3-D shapes. IEEE Trans. Pattern Anal. Mach. Intell. (PAMI) 14(2), 239–256 (1992)CrossRef
2.
3.
Bylow, E., Sturm, J., Kerl, C., Kahl, F., Cremers, D.: Real-time camera tracking and 3D reconstruction using signed distance functions. In: Robotics: Science and Systems Conference (RSS) (2013)
4.
5.
Canelhas, D.R., Stoyanov, T., Lilienthal, A.J.: SDF tracker: a parallel algorithm for on-line pose estimation and scene reconstruction from depth images. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2013)
6.
Chen, Y., Medioni, G.: Object modeling by registration of multiple range images. In: Proceedings of the 1991 IEEE International Conference on Robotics and Automation (ICRA), vol. 3, pp. 2724–2729 (1991)
7.
Claes, P., Vandermeulen, D., Van Gool, L., Suetens, P.: Robust and accurate partial surface registration based on variational implicit surfaces for automatic 3D model building. In: Fifth International Conference on 3-D Digital Imaging and Modeling (3DIM) (2005)
8.
9.
Curless, B., Levoy, M.: A volumetric method for building complex models from range images. In: Proceedings of the 23rd Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1996, pp. 303–312 (1996)
10.
Dimashova, M., Lysenkov, I., Rabaud, V., Eruhimov, V.: Tabletop object scanning with an RGB-D sensor. In: Third Workshop on Semantic Perception, Mapping and Exploration (SPME) at the 2013 IEEE International Conference on Robotics and Automation (ICRA) (2013)
11.
Handa, A., Whelan, T., McDonald, J., Davison, A.J.: A benchmark for RGB-D visual odometry, 3D reconstruction and SLAM. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)
12.
Izadi, S., Kim, D., Hilliges, O., Molyneaux, D., Newcombe, R., Kohli, P., Shotton, J., Hodges, S., Freeman, D., Davison, A., Fitzgibbon, A.: KinectFusion: real-time 3D reconstruction and interaction using a moving depth camera. In: ACM Symposium on User Interface Software and Technology (UIST) (2011)
13.
Kehl, W., Navab, N., Ilic, S.: Coloured signed distance fields for full 3D object reconstruction. In: Proceedings of the British Machine Vision Conference (BMVC) (2014)
14.
15.
Kerl, C., Sturm, J., Cremers, D.: Robust odometry estimation for RGB-D cameras. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA) (2013)
16.
Kubacki, D.B., Bui, H.Q., Babacan, S.D., Do, M.N.: Registration and integration of multiple depth images using signed distance function. In: SPIE Proceedings, vol. 8296 (2012)
17.
Kümmerle, R., Grisetti, G., Strasdat, H., Konolige, K., Burgard, W.: g\(^{2}\)o: a general framework for graph optimization. In: Proceedings of the IEEE International Conference on Robotics and Automation (ICRA), pp. 3607–3613, May 2011
18.
Lai, K., Bo, L., Ren, X., Fox, D.: A large-scale hierarchical multi-view RGB-D object dataset. In: IEEE International Conference on Robotics and Automation (ICRA) (2011)
19.
Lorensen, W.E., Cline, H.E.: Marching cubes: a high resolution 3D surface construction algorithm. In: Proceedings of the 14th Annual Conference on Computer Graphics and Interactive Techniques, SIGGRAPH 1987, pp. 163–169 (1987)
20.
Lucas, B., Kanade, T.: An iterative image registration technique with an application to stereo vision. In: Proceedings of the 7th International Joint Conference on Artificial Intelligence, IJCAI 1981, vol. 2, pp. 674–679 (1981)
21.
Ma, Y., Soatto, S., Košecká, J., Sastry, S.S.: An Invitation to 3-D Vision: From Images to Geometric Models. Springer, New York (2003)MATH
22.
Masuda, T.: Registration and integration of multiple range images by matching signed distance fields for object shape modeling. Comput. Vis. Image Underst. (CVIU) 87(1–3), 51–65 (2002)CrossRefMATH
23.
Narayan, K.S., Sha, J., Singh, A., Abbeel, P.: Range sensor and silhouette fusion for high-quality 3D scanning. In: IEEE International Conference on Robotics and Automation (ICRA) (2015)
24.
Newcombe, R.A., Izadi, S., Hilliges, O., Molyneaux, D., Kim, D., Davison, A.J., Kohli, P., Shotton, J., Hodges, S., Fitzgibbon, A.: KinectFusion: real-time dense surface mapping and tracking. In: 10th International Symposium on Mixed and Augmented Reality (ISMAR) (2011)
25.
Osher, S., Fedkiw, R.: Level Set Methods and Dynamic Implicit Surfaces. Applied Mathematical Science, vol. 153. Springer, New York (2003)MATH
26.
Paragios, N., Rousson, M., Ramesh, V.: Non-rigid registration using distance functions. Comput. Vis. Image Underst. 89(2–3), 142–165 (2003)CrossRefMATH
27.
28.
Pomerleau, F., Colas, F., Siegwart, R., Magnenat, S.: Comparing ICP variants on real-world data sets - open-source library and experimental protocol. Auton. Robots 34(3), 133–148 (2013)CrossRef
29.
Pomerleau, F., Liu, M., Colas, F., Siegwart, R.: Challenging data sets for point cloud registration algorithms. Int. J. Robot. Res. (IJRR) 31(14), 1705–1711 (2012)CrossRef
30.
Rouhani, M., Sappa, A.D.: The richer representation the better registration. IEEE Trans. Image Proc. 22(12), 5036–5049 (2013)MathSciNetCrossRef
31.
Rusinkiewicz, S., Levoy, M.: Efficient variants of the ICP algorithm. In: 3rd International Conference on 3D Digital Imaging and Modeling (3DIM) (2001)
32.
Rusu, R.B., Holzbach, A., Blodow, N., Beetz, M.: Fast geometric point labeling using conditional random fields. In: IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) (2009)
33.
Segal, A., Haehnel, D., Thrun, S.: Generalized-ICP. In: Proceesings of Robotics: Science and Systems (RSS) (2009)
34.
Singh, A., Sha, J., Narayan, K., Achim, T., Abbeel, P.: BigBIRD: a large-scale 3D database of object instances. In: IEEE International Conference on Robotics and Automation (ICRA) (2014)
35.
36.
Steinbrücker, F., Sturm, J., Cremers, D.: Real-time visual odometry from dense RGB-D images. In: 2011 IEEE International Conference on Computer Vision Workshops (ICCV Workshops) (2011)
37.
Stoyanov, T., Magnusson, M., Lilienthal, A.: Point set registration through minimization of the \(L_2\) distance between 3D-NDT models. In: IEEE International Conference on Robotics and Automation (ICRA) (2012)
38.
Sturm, J., Engelhard, N., Endres, F., Burgard, W., Cremers, D.: A benchmark for the evaluation of RGB-D SLAM systems. In: Proceedings of the International Conference on Intelligent Robot Systems (IROS) (2012)
39.
Tomasi, C., Manduchi, R.: Bilateral filtering for Gray and color images. In: Sixth IEEE International Conference on Computer Vision (ICCV), pp. 839–846 (1998)
40.
Tubic, D., Hébert, P., Laurendeau, D.: A volumetric approach for interactive 3D modeling. Comput. Vis. Image Underst. (CVIU) 92(1), 56–77 (2003)CrossRef
41.
Vijayanagar, K.R., Loghman, M., Kim, J.: Real-time refinement of Kinect depth maps using multi-resolution anisotropic diffusion. Mobile Netw. Appl. 19(3), 414–425 (2014)CrossRef
42.
Zach, C., Pock, T., Bischof, H.: A globally optimal algorithm for robust TV-\(L^1\) range image integration. In: Proceedings of the 11th IEEE International Conference on Computer Vision (ICCV), pp. 1–8 (2007)
43.
ZCorporation: ZPrinter 650. Hardware manual (2008)
44.
Zhou, Q., Koltun, V.: Dense scene reconstruction with points of interest. ACM Trans. Graph. 32(4), 112:1–112:8 (2013)MATH
Metadaten
Titel
SDF-2-SDF: Highly Accurate 3D Object Reconstruction
verfasst von
Miroslava Slavcheva
Wadim Kehl
Nassir Navab
Slobodan Ilic
Copyright-Jahr
2016
Buch
Computer Vision – ECCV 2016

Print ISBN: 978-3-319-46447-3

Electronic ISBN: 978-3-319-46448-0

Copyright-Jahr: 2016

DOI
https://doi.org/10.1007/978-3-319-46448-0_41