30 January 2012 2D-3D feature association via projective transform invariants for model-based 3D pose estimation
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Abstract
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.
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O. Serdar Gedik, O. Serdar Gedik, A. Aydin Alatan, A. Aydin Alatan, "2D-3D feature association via projective transform invariants for model-based 3D pose estimation", Proc. SPIE 8290, Three-Dimensional Image Processing (3DIP) and Applications II, 82900R (30 January 2012); doi: 10.1117/12.910463; https://doi.org/10.1117/12.910463
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