
\section{Conclusions}
Currently the field of camera pose estimation is hindered by a lack of challenging and realistic evaluation benchmarks. 
Many recent methods have claimed advantages over older, classic methods such as COLMAP by evaluating on currently available benchmarks, but these benchmarks are largely synthetic or too simple. 
% As a result, we have observed a discrepancy between which methods are used in practice and for various applications (COLMAP) and which methods are used as building blocks for achieving SOTA camera estimation. 
This lack of a proper benchmark can potentially lead researchers into wayward directions while not addressing the current issues faced by real-world users. 

We proposed a new real-world SfM benchmark, ORBIT, derived from 360° videos, to try to address this problem.
% Our proposed evaluation benchmark is small and easy to evaluate on, 
ORBIT is challengingly diverse, including many indoor and outdoor scenes with few or many moving objects alongside moving camera trajectories with ground truth derived from our rig-based SfM pipeline. The camera trajectories themselves are also diverse with loops as well as straight paths, and includes varying camera rotation as well as translation.
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%\RS{Do we want to mention future work, e.g., generating test clips with different FOVs to see how this affects performance?}
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Our analysis on current SOTA methods shows that there is a large performance gap to be reached and many challenges are still left open and answered. 
ORBIT can also be expanded to include more challenges in the future, e.g., varying intrinsics such as focal length across and within clips.
% Therefore, requiring SFM methods to also optimize for the intrinsic matrix.
