three cases are all more than 50%. The RANSAC-based methods perform poorly compared with the global optimization methods. One thing to note is that in real world dataset, dimension-wise voting brings slight performance drop, but still achieves superior performance against comparative methods. Also note that in Exp03, the proposed method gives optimistic results by regarding 2 outliers as inliers, which may be caused by unknown noise bound thus inappropriate threshold in real world data. In Exp04, Exp05 and Exp06, the utilization of good line features promotes the performance of point line methods obviously (2-Entity and ours). Overall, the results still confirm the conclusions in simulation. **Full dataset performance.** Finally, we arrive at the success rate on the whole three sessions as shown in Fig. \[fig.session\]. As LMI is too slow to finish all the dataset, here we only show the result of ours and RANSAC methods. We first see that the proposed globally optimal methods consistently outperform the RANSAC methods on all three sessions. The other fact is that progressive prioritized voting brings the best accuracy over the one with dimension-wise voting, because of the consideration on extremely low number of inliers.

Conclusions
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In this paper, we propose a 