Covariance Propagation and Next Best View Planning for 3D Reconstruction

Summary, in English

This paper examines the potential benefits of applying next best view planning to sequential 3D reconstruction from unordered image sequences. A standard sequential structure-and-motion pipeline is extended with active selection of the order in which cameras are resectioned. To this end, approximate covariance propagation is implemented throughout the system, providing running estimates of the uncertainties of the reconstruction, while also enhancing robustness and accuracy. Experiments show that the use of expensive global bundle adjustment can be reduced throughout the process, while the additional cost of propagation is essentially linear in the problem size.

• Författare: Sebastian Haner; Anders Heyden
• Redaktör: Andrew Fitzgibbon; Svetlana Lazebnik; Pietro Perona; Yoichi Sato; Cordelia Schmid
• Avdelning/ar: Matematik LTH; Mathematical Imaging Group; ELLIIT: the Linköping-Lund initiative on IT and mobile communication
• Publiceringsår: 2012
• Språk: Engelska
• Sidor: 545-556
• Publikation/Tidskrift/Serie: Computer Vision – ECCV 2012 12th European Conference on Computer Vision, Florence, Italy, October 7-13, 2012, Proceedings, Part II (Lecture Notes in Computer Science 7573)
• Volym: 7573
• Fulltext: - 2 MB; Download statistics
• Länkar: http://link.springer.com/chapter/10.1007/978-3-642-33709-3_39; http://dx.doi.org/10.1007/978-3-642-33709-3_39
• Dokumenttyp: Konferensbidrag
• Förlag: Springer
• Ämne: Computer Vision and Robotics (Autonomous Systems); Mathematics
• Conference name: 12th European Conference on Computer Vision (ECCV 2012)
• Conference date: 2012-10-07 - 2012-10-13
• Conference place: Florence, Italy
• Status: Published
• Forskningsgrupp: Mathematical Imaging Group
• ISBN/ISSN/Övrigt: ISSN: 0302-9743; ISSN: 1611-3349; ISBN: 978-3-642-33709-3; ISBN: 978-3-642-33708-6 (Print)