by incorrectly regarding outliers as inliers. In addition, both RANSAC and LMI fail when the outlier rate is 90%. The results for all 100 runs when the outlier rate is 80% are also shown in Fig. \[fig.robustness\]. We can see that the proposed algorithm deterministically finds the globally optimal consensus, while RANSAC achieves global optimality probabilistically.

**Robustness and accuracy.** We finally show the performance of all methods on the synthetic data, including accuracy, precision and recall to measure the CCI, with respect to percentage of outliers ranging from 60% to 90%. Note that we only evaluate the accuracy for successful trials, since result on incorrectly identified consensus set can lead to very large error, disturbing the accuracy. The result in Tab. \[table.robustness\] first confirms that CCI is highly related to the accuracy, validating the feasibility of maximizing consensus set. RANSAC gives consistent conservative estimations, as the precision remains at a higher level compared with the recall. For LMI, the estimation is prone to regard the outliers as inliers, thus the recall is higher compared with precision. Considering that LMI, P3P and EPnP are designed for general visual localization, the better performance achieved by 2-Entity and the proposed method, designed for 