is an inlier.

The main advantage of R1PP$n$P is that it has much lower time complexity and is much faster than conventional RANSAC+P3P or P4P methods, especially when the percentage of outliers is large. Compared with REPP$n$P, the proposed R1PP$n$P does not suffer from the percentage of outliers limitation.

This paper is organized as follows. In Section II, we describe the fundamental model used in R1PP$n$P. The details of the core algorithm are given in Section III, in which we also provide its proof of convergence, local minima analysis and the strategy to select control points. The outliers handling mechanism, including soft weight assignment and the 1-point RANSAC scheme, is introduced in Section IV. We also provide details of termination conditions in Section IV. Evaluation results are presented in Section V. A discussion and description of planned future work is described in Section VI.

Related Works
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The P$n$P problem, coined by Fischler and Bolles [@fischler1981random], is articulated as follows: *Given the relative spatial locations of $n$ control points, and given the angle to every pair of control points $P_i$ from an additional point called the center of perspective $C$, find the lengths of the line segments joining $C$ to 