  We consider the problem of relative pose regression in visual relocalization.
Recently, several promising approaches have emerged in this area. We claim that
even though they demonstrate on the same datasets using the same split to train
and test, a faithful comparison between them was not available since on
currently used evaluation metric, some approaches might perform favorably,
while in reality performing worse. We reveal a tradeoff between accuracy and
the 3D volume of the regressed subspace. We believe that unlike other
relocalization approaches, in the case of relative pose regression, the
regressed subspace 3D volume is less dependent on the scene and more affect by
the method used to score the overlap, which determined how closely sampled
viewpoints are. We propose three new metrics to remedy the issue mentioned
above. The proposed metrics incorporate statistics about the regression
subspace volume. We also propose a new pose regression network that serves as a
new baseline for this task. We compare the performance of our trained model on
Microsoft 7-Scenes and Cambridge Landmarks datasets both with the standard
metrics and the newly proposed metrics and adjust the overlap score to reveal
the tradeoff between the subspace and performance. The results show that the
proposed metrics are more robust to different overlap threshold than the
conventional approaches. Finally, we show that our network generalizes well,
specifically, training on a single scene leads to little loss of performance on
the other scenes.
