Comparison between asymptotic Bayesian approach and Kalman filter-based technique for 3D reconstruction using an image sequence

Chun-Jen Tsai*, Yi Ping Hung, Shun Chin Hsu

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

5 Scopus citations

Abstract

Reconstructing 3D informations of a scene from a sequence of 2D images is an important problem in computer vision. This paper compares two statistical approaches for 3D reconstruction from an image sequence: the asymptotic Bayesian surface reconstruction and the Kalman filter-based depth estimation. Both techniques are recursive algorithms where relevant information contained in previously taken images are summarized in a prior term (prior to the taking of the next image), which means that the reconstruction results are based upon informations from all images but the storage and computation required do not grow dramatically. The experiments with both real images and computer generated images demonstrate that the asymptotic Bayesian approach achieve better results than the Kalman filter-based approach does, mainly due to the better problem formulation.

Original languageEnglish
Title of host publicationIEEE Computer Vision and Pattern Recognition
Editors Anon
PublisherPubl by IEEE
Pages206-211
Number of pages6
ISBN (Print)0818638826
DOIs
StatePublished - 1 Dec 1993
EventProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition - New York, NY, USA
Duration: 15 Jun 199318 Jun 1993

Publication series

NameIEEE Computer Vision and Pattern Recognition

Conference

ConferenceProceedings of the 1993 IEEE Computer Society Conference on Computer Vision and Pattern Recognition
CityNew York, NY, USA
Period15/06/9318/06/93

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    Tsai, C-J., Hung, Y. P., & Hsu, S. C. (1993). Comparison between asymptotic Bayesian approach and Kalman filter-based technique for 3D reconstruction using an image sequence. In Anon (Ed.), IEEE Computer Vision and Pattern Recognition (pp. 206-211). (IEEE Computer Vision and Pattern Recognition). Publ by IEEE. https://doi.org/10.1109/CVPR.1993.340959