AEP$n$P: A Less-constrained EP$n$P Solver for Pose Estimation with Anisotropic Scaling
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arXiv
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| Format: | Preprint |
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2023
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| _version_ | 1866914887586807808 |
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| author | Wei, Jiaxin Leutenegger, Stefan Kneip, Laurent |
| author_facet | Wei, Jiaxin Leutenegger, Stefan Kneip, Laurent |
| contents | Perspective-$n$-Point (P$n$P) stands as a fundamental algorithm for pose estimation in various applications. In this paper, we present a new approach to the P$n$P problem with relaxed constraints, eliminating the need for precise 3D coordinates, which is especially suitable for object pose estimation where corresponding object models may not be available in practice. Built upon the classical EP$n$P solver, we refer to it as AEP$n$P due to its ability to handle unknown anisotropic scaling factors in addition to the common 6D transformation. Through a few algebraic manipulations and a well-chosen frame of reference, this new problem can be boiled down to a simple linear null-space problem followed by point registration-based identification of a similarity transformation. Experimental results on both simulated and real datasets demonstrate the effectiveness of AEP$n$P as a flexible and practical solution to object pose estimation. Code: https://github.com/goldoak/AEPnP. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2310_09982 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | AEP$n$P: A Less-constrained EP$n$P Solver for Pose Estimation with Anisotropic Scaling Wei, Jiaxin Leutenegger, Stefan Kneip, Laurent Computer Vision and Pattern Recognition Perspective-$n$-Point (P$n$P) stands as a fundamental algorithm for pose estimation in various applications. In this paper, we present a new approach to the P$n$P problem with relaxed constraints, eliminating the need for precise 3D coordinates, which is especially suitable for object pose estimation where corresponding object models may not be available in practice. Built upon the classical EP$n$P solver, we refer to it as AEP$n$P due to its ability to handle unknown anisotropic scaling factors in addition to the common 6D transformation. Through a few algebraic manipulations and a well-chosen frame of reference, this new problem can be boiled down to a simple linear null-space problem followed by point registration-based identification of a similarity transformation. Experimental results on both simulated and real datasets demonstrate the effectiveness of AEP$n$P as a flexible and practical solution to object pose estimation. Code: https://github.com/goldoak/AEPnP. |
| title | AEP$n$P: A Less-constrained EP$n$P Solver for Pose Estimation with Anisotropic Scaling |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2310.09982 |