@inproceedings{d14ebe30401a4fafa3e0080cc2678a7d,
title = "AEPnP: A Less-Constrained EPnP Solver for Pose Estimation with Anisotropic Scaling",
abstract = "Perspective-n-Point (PnP) stands as a fundamental algorithm for pose estimation in various applications. In this paper, we present a new approach to the PnP problem with relaxed constraints, eliminating the need for precise 3D coordinates, which is especially suitable for object pose estimation where corresponding object models may not be available in practice. Built upon the classical EPnP solver, we refer to it as AEPnP due to its ability to handle unknown anisotropic scaling factors in addition to the common 6D transformation. Through a few algebraic manipulations and a well-chosen frame of reference, this new problem can be boiled down to a simple linear null-space problem followed by point registration-based identification of a similarity transformation. Experimental results on both simulated and real datasets demonstrate the effectiveness of AEPnP as a flexible and practical solution to object pose estimation. Code: https://github.com/goldoak/AEPnP.",
keywords = "Object pose estimation, Perspective-n-Point",
author = "Jiaxin Wei and Stefan Leutenegger and Laurent Kneip",
note = "Publisher Copyright: {\textcopyright} The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.; Workshops that were held in conjunction with the 18th European Conference on Computer Vision, ECCV 2024 ; Conference date: 29-09-2024 Through 04-10-2024",
year = "2025",
doi = "10.1007/978-3-031-91989-3\_3",
language = "English",
isbn = "9783031919886",
series = "Lecture Notes in Computer Science",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "37--50",
editor = "\{Del Bue\}, Alessio and Cristian Canton and Jordi Pont-Tuset and Tatiana Tommasi",
booktitle = "Computer Vision {\textendash} ECCV 2024 Workshops, Proceedings",
}