  We consider a single-query 6-DoF camera pose estimation with reference images
and a point cloud, i.e. the problem of estimating the position and orientation
of a camera by using reference images and a point cloud. In this work, we
perform a systematic comparison of three state-of-the-art strategies for 6-DoF
camera pose estimation, i.e. feature-based, photometric-based and
mutual-information-based approaches. The performance of the studied methods is
evaluated on two standard datasets in terms of success rate, translation error
and max orientation error. Building on the results analysis, we propose a
hybrid approach that combines feature-based and mutual-information-based pose
estimation methods since it provides complementary properties for pose
estimation. Experiments show that (1) in cases with large environmental
variance, the hybrid approach outperforms feature-based and
mutual-information-based approaches by an average of 25.1% and 5.8% in terms of
success rate, respectively; (2) in cases where query and reference images are
captured at similar imaging conditions, the hybrid approach performs similarly
as the feature-based approach, but outperforms both photometric-based and
mutual-information-based approaches with a clear margin; (3) the feature-based
approach is consistently more accurate than mutual-information-based and
photometric-based approaches when at least 4 consistent matching points are
found between the query and reference images.
