inlier to make the algorithm work. Hence, we employ the 1-point RANSAC scheme to try different 3D-2D correspondences as the control point until the algorithm finds the correct solution. The 1-point RANSAC scheme combines the core algorithm naturally because the core algorithm can perform the computation with any control point $o \in [1,N]$. We assume that 2D-3D correspondences have the same the possibility to be an inlier, without loss of generality, we select the control point $o$ from the center of all image points to the outside. This is because we found that R1PP$n$P needs less iterations to converge when $o$ is closer to the center, the details of which has been discussed in Section 3.3.

Algorithm Flow Chart
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In general, the overall flow chart of R1PP$n$P is shown in Fig. \[fig\_algorithmstep\], we first detect as many inliers as possible inside the RANSAC framework, then based on the detected inliers, we perform the R1PP$n$P algorithm without re-weighting mechanism to get more accurate results.

\[ht\] ![The overall flow chart of R1PP$n$P.[]{data-label="fig_algorithmstep"}](figures/MethodSteps.pdf "fig:"){width=".48\textwidth"}

Appropriate termination conditions seek balance between speed and precision for RANSAC-based or iterative algorithms. As shown in Fig. \[fig\_algorithmstep\], two types of termination conditions need to be specified 