The 2D image projections are generated with randomly sampled camera poses in $[-2,2]^3 \times [-\pi,\pi]^3$, as well as their inlier correspondences. All the projected 2D image points are added with bounded random noise $e_{i}$ with the bound $n_i=2$. Each outlier correspondence is generated from other randomly sampled camera pose different to ground truth pose. The total number of correspondences is fixed as 50. Specifically, there are 50 point correspondences when evaluating point only methods, while 25 point and 25 line correspondences for the point and line methods. We vary the outlier percentage from 10% to 90% with a step of 10%. Statistic performance indicators are evaluated with an average of 100 Monte Carlo runs. Denoting the ground truth pose as $[R_{gt}|t_{gt}]$, we compute the translation error as $\triangle T=|\hat{t}-t_{gt}|$ in meter and the rotation error as the angle of $\triangle R=\hat{R}R_{gt}^{T}$ in degree.

**BnB heuristics.** We first evaluate the heuristics introduced in Section \[er\] from the aspect of accuracy and efficiency. As shown in Fig. \[fig.yawTime\], with the heuristics, the efficiency is improved while the accuracy stays similar. Since the final pose is refined by nonlinear optimization, slight rotation error after BnB can be ignored. As a baseline, we also 