This paper proposes a Hybrid Approximate Representation (HAR) based on unifying several efficient approximations of the generalized reprojection error (which is known as the gold standard for multiview geometry). The HAR is an over-parameterization scheme where the approximation is applied simultaneously in multiple parameter spaces. A joint minimization scheme “HAR-Descent” can then solve the PnP problem efficiently, while remaining robust to approximation errors and local minima. The technique is evaluated extensively, including numerous synthetic benchmark protocols and the real-world data evaluations used in previous works. The proposed technique was found to have runtime complexity comparable to the fastest O(n)O(n) techniques, and up to 10 times faster than current state of the art minimization approaches. In addition, the accuracy exceeds that of all 9 previous techniques tested, providing definitive state of the art performance on the benchmarks, across all 90 of the experiments in the paper and supplementary material, which can be found on the Computer Society Digital Library at http://doi.ieeecomputersociety.org/10.1109/TPAMI.2018.2806446.

中文翻译：

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HARD-PnP：使用混合近似表示的 PnP 优化


本文提出了一种基于统一广义重投影误差的几种有效近似（被称为多视图几何的黄金标准）的混合近似表示（HAR）。 HAR 是一种超参数化方案，其中近似同时应用于多个参数空间。联合最小化方案“HAR-Descent”可以有效地解决 PnP 问题，同时对近似误差和局部最小值保持鲁棒性。该技术得到了广泛的评估，包括许多综合基准协议和先前作品中使用的真实世界数据评估。研究发现，所提出的技术的运行时复杂度与最快的 O(n)O(n) 技术相当，并且比当前最先进的最小化方法快 10 倍。此外，其准确性超过了之前测试的所有 9 种技术，在论文和补充材料中的所有 90 项实验中，在基准测试中提供了最先进的性能，这些实验和补充材料可以在计算机协会数字图书馆 http://www.computer Society Digital Library 中找到。 ://doi.ieeecomputersociety.org/10.1109/TPAMI.2018.2806446。