[en] One of the most crucial requirements for building a multi-view system is the estimation of relative poses of all cameras. An approach tailored for a RGB-D cameras based multi-view system is missing. We propose BAICP+ which combines Bundle Adjustment (BA) and Iterative Closest Point (ICP) algorithms to take into account both 2D visual and 3D shape information in one minimization formulation to estimate relative pose parameters of each camera. BAICP+ is generic enough to take different types of visual features into account and can be easily adapted to varying quality of 2D and 3D data. We perform experiments on real and simulated data. Results show that with the right weighting factor BAICP+ has an optimal performance when compared to BA and ICP used independently or sequentially.
Disciplines :
Sciences informatiques
Auteur, co-auteur :
AFZAL, Hassan ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
AOUADA, Djamila ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Fofi, David; Universite de Bourgogne
Mirbach, Bruno
OTTERSTEN, Björn ; University of Luxembourg > Interdisciplinary Centre for Security, Reliability and Trust (SNT)
Langue du document :
Anglais
Titre :
RGB-D Multi-View System Calibration for Full 3D Scene Reconstruction
Date de publication/diffusion :
2014
Nom de la manifestation :
22nd International Conference on Pattern Recognition
Date de la manifestation :
from 24-08-2014 to 28-08-2014
Manifestation à portée :
International
Titre de l'ouvrage principal :
22nd International Conference on Pattern Recognition (ICPR'14)
P. Besl and N. D. McKay. A method for registration of 3-D shapes. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 14(2):239-256, Feb.
J. Bougouet. http://www. vision. caltech. edu/bouguetj/calibdoc/, 2007.
G. Bradski. Dr. Dobb's Journal of Software Tools
E. Bylow, J. Sturm, C. Kerl, F. Kahl, and D. Cremers. Real-Time Camera Tracking and 3D Reconstruction Using Signed Distance Functions. In Robotics: Science and Systems Conference (RSS), June 2013.
F. Endres, J. Hess, N. Engelhard, J. Sturm, D. Cremers, and W. Burgard. An evaluation of the RGB-D SLAM system. In ICRA, pages 1691-1696. IEEE, 2012.
R. Furuakwa, K. Inose, and H. Kawasaki. Multi-view reconstruction for projector camera systems based on bundle adjustment. In Computer Vision and Pattern Recognition Workshops, 2009. CVPR Workshops 2009. IEEE Computer Society Conference on, pages 69-76, 2009.
P. Henry, M. Krainin, E. Herbst, X. Ren, and D. Fox. RGB-D Mapping: Using Kinect-Style Depth Cameras for Dense 3D Modeling of Indoor Environments. International Journal of Robotics Research (IJRR), 31(5):647-663, April 2012.
A. Johnson and S. B. Kang. Registration and Integration of Textured 3-D Data. In InternationalConference on Recent Advances in 3-D Digital Imaging and Modeling (3DIM '97), pages 234-241, May 1997.
B. Kainz, S. Hauswiesner, G. Reitmayr, M. Steinberger, R. Grasset, L. Gruber, E. Veas, D. Kalkofen, H. Seichter, and D. Schmalstieg. OmniKinect: Real-Time Dense Volumetric Data Acquisition and Applications. In Proceedings of the 18th ACM symposium on Virtual reality software and technology, VRST '12, pages 25-32, New York, NY, USA, 2012. ACM.
I. Y. Kim and O. L. de Weck. Adaptive weighted-sum method for bi-objective optimization: Pareto front generation. Structural and Multidisciplinary Optimization, 29(2):149-158, February 2005.
C. Kuster, T. Popa, C. Zach, C. Gotsman, and M. Gross. FreeCam: A Hybrid Camera System for Interactive Free-Viewpoint Video. In Proceedings of Vision, Modeling, and Visualization (VMV), 2011.
A. Maimone and H. Fuchs. Encumbrance-Free Telepresence System with Real-Time 3D Capture and Display using Commodity Depth Cameras. In Mixed and Augmented Reality (ISMAR), 2011 10th IEEE International Symposium on, pages 137-146, Oct.
D. Neumann, F. Lugauer, S. Bauer, J. Wasza, and J. Hornegger. Realtime RGB-D mapping and 3-D modeling on the GPU using the random ball cover data structure. In ICCV Workshops, pages 1161-1167. IEEE, 2011.
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohli, J. Shotton, S. Hodges, and A. Fitzgibbon. KinectFusion: Real-Time Dense Surface Mapping and Tracking. In Proceedings of the 2011 10th IEEE International Symposium on Mixed and Augmented Reality, ISMAR '11, pages 127-136, Washington, DC, USA, 2011. IEEE Computer Society.
K. Ni, D. Steedly, and F. Dellaert. Out-of-Core Bundle Adjustment for Large-Scale 3D Reconstruction. In ICCV, pages 1-8. IEEE, 2007.
A. Nüchter, J. Elseberg, P. Schneider, and D. Paulus. Study of parameterizations for the rigid body transformations of the scan registration problem. Computer Vision and Image Understanding, 114(8):963-980, 2010.
B. Penelle, A. Schenkel, and N. Warzee. Geometrical 3D Reconstruction using Real-Time RGB-D Cameras. In 3D Imaging (IC3D), 2011 International Conference on, pages 1-8, 2011.
R. B. Rusu and S. Cousins. 3D is here: Point Cloud Library (PCL). In International Conference on Robotics and Automation, Shanghai, China, 2011 2011.
R. Szeliski. Computer Vision: Algorithms and Applications. Springer-Verlag New York, Inc., New York, NY, USA, 1st edition, 2010.
J. Tong, J. Zhou, L. Liu, Z. Pan, and H. Yan. Scanning 3D Full Human Bodies Using Kinects. IEEE Transactions on Visualization and Computer Graphics, 18(4):643-650, Apr. 2012.
B. Triggs, P. Mclauchlan, R. Hartley, and A. Fitzgibbon. Bundle adjustment A Modern Synthesis. In Vision Algorithms: Theory and Practice, LNCS, pages 298-375. Springer Verlag, 2000.
