ImPosIng: Implicit Pose Encoding for Efficient Camera Pose Estimation

• URL: http://arxiv.org/abs/2205.02638v1
• Date: Thu, 5 May 2022 13:33:25 GMT
• Title: ImPosIng: Implicit Pose Encoding for Efficient Camera Pose Estimation
• Authors: Arthur Moreau, Thomas Gilles, Nathan Piasco, Dzmitry Tsishkou, Bogdan Stanciulescu, Arnaud de La Fortelle
• Abstract summary: Implicit Pose. (ImPosing) embeds images and camera poses into a common latent representation with 2 separate neural networks. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but refined.
• Score: 2.6808541153140077
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a novel learning-based formulation for camera pose estimation that can perform relocalization accurately and in real-time in city-scale environments. Camera pose estimation algorithms determine the position and orientation from which an image has been captured, using a set of geo-referenced images or 3D scene representation. Our new localization paradigm, named Implicit Pose Encoding (ImPosing), embeds images and camera poses into a common latent representation with 2 separate neural networks, such that we can compute a similarity score for each image-pose pair. By evaluating candidates through the latent space in a hierarchical manner, the camera position and orientation are not directly regressed but incrementally refined. Compared to the representation used in structure-based relocalization methods, our implicit map is memory bounded and can be properly explored to improve localization performances against learning-based regression approaches. In this paper, we describe how to effectively optimize our learned modules, how to combine them to achieve real-time localization, and demonstrate results on diverse large scale scenarios that significantly outperform prior work in accuracy and computational efficiency.

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