2D-3D feature association via projective transform invariants for model-based 3D pose estimation

Date

2012-01-26

Author

Gedik, O. Serdar
Alatan, Abdullah Aydın

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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The three dimensional (3D) tracking of rigid objects is required in many applications, such as 3D television (3DTV) and augmented reality. Accurate and robust pose estimates enable improved structure reconstructions for 3DTV and reduce jitter in augmented reality scenarios. On the other hand, reliable 2D-3D feature association is one of the most crucial requirements for obtaining high quality 3D pose estimates. In this paper, a 2D-3D registration method, which is based on projective transform invariants, is proposed. Due to the fact that projective transform invariants are highly dependent on 2D and 3D coordinates, the proposed method relies on pose consistencies in order to increase robustness of 2D-3D association. The reliability of the approach is shown by comparisons with RANSAC, perspective factorization and SoftPOSIT based methods on real and artificial data.

Subject Keywords

2D-3D feature association, Model-based 3D pose estimation, Projective transform invariants

URI

https://hdl.handle.net/11511/41406

DOI

https://doi.org/10.1117/12.910463

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

O. S. Gedik and A. A. Alatan, “2D-3D feature association via projective transform invariants for model-based 3D pose estimation,” 2012, vol. 8290, Accessed: 00, 2020. [Online]. Available: https://hdl.handle.net/11511/41406.