Abstract
In this paper, we present a novel non-iterative algorithm for solving the pose estimation problem from a set of 3D-to-2D point correspondences, known as the Perspective-n-Point (PnP) problem. The presented algorithm is capable of achieving both geometrical and statistical optimality by exploring the geometrical constraints of the PnP problem through a nonlinear least-squares fashion, as well as accounting for observation uncertainty in the solution process. In addition, to further improve the accuracy of the presented algorithm, we introduce a method that is able to eliminate the bias of solution caused by the propagation of uncertainty, resulting in a consistent estimate. Experimental tests on synthetic data and real images (i.e., TempleRing dataset) show that the presented algorithm can well adapt to different levels of noise, and out-perform state-of-the-art (SOTA) PnP algorithms in terms of accuracy and computational cost. This makes the presented algorithm eminently suitable for a wide range of application scenarios.
This work is supported in part by the Startup Foundation for Introducing Talent of NUIST under Grant 2022r078.
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Zhou, X., Xie, Z., Yu, Q., Zong, Y., Wang, Y. (2024). An Efficient and Consistent Solution to the PnP Problem. In: Liu, Q., et al. Pattern Recognition and Computer Vision. PRCV 2023. Lecture Notes in Computer Science, vol 14426. Springer, Singapore. https://doi.org/10.1007/978-981-99-8432-9_17
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DOI: https://doi.org/10.1007/978-981-99-8432-9_17
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