The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose
  Refinement
Gabriele Trivigno Carlo Masone Barbara Caputo Torsten Sattler
Pose refinement is an interesting and practically relevant research
direction. Pose refinement can be used to (1) obtain a more accurate pose
estimate from an initial prior (e.g., from retrieval), (2) as pre-processing,
i.e., to provide a better starting point to a more expensive pose estimator,
(3) as post-processing of a more accurate localizer. Existing approaches focus
on learning features / scene representations for the pose refinement task. This
involves training an implicit scene representation or learning features while
optimizing a camera pose-based loss. A natural question is whether training
specific features / representations is truly necessary or whether similar
results can be already achieved with more generic features. In this work, we
present a simple approach that combines pre-trained features with a particle
filter and a renderable representation of the scene. Despite its simplicity, it
achieves state-of-the-art results, demonstrating that one can easily build a
pose refiner without the need for specific training. The code is at
https://github.com/ga1i13o/mcloc_poseref