The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement
<|reference_start|>The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement: 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<|reference_end|>
@article{trivigno2024the,
    title={The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose
  Refinement},
    author={Gabriele Trivigno, Carlo Masone, Barbara Caputo, Torsten Sattler},
    journal={arXiv preprint arXiv:2404.10438},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.10438},
    primaryClass={cs.CV}
}