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 Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement / Trivigno, Gabriele; Masone, Carlo; Caputo, Barbara; Sattler, Torsten. - (2024), pp. 12786-12798. (Intervento presentato al convegno IEEE / CVF Computer Vision and Pattern Recognition Conference (CVPR) tenutosi a Seattle (USA) nel 16-22 June 2024) [10.1109/CVPR52733.2024.01215].

The Unreasonable Effectiveness of Pre-Trained Features for Camera Pose Refinement

Gabriele Trivigno;Carlo Masone;Barbara Caputo;
2024

Abstract

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.
2024
979-8-3503-5300-6
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2989656