上海科技大学知识管理系统

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 or complete calibration data. We refer to it as APnP due to its ability to handle unknown anisotropic scaling factors of 3D coordinates or alternatively two distinct focal lengths in addition to the conventional rigid transformation. Through algebraic manipulations and a novel parametrization, both cases are brought into similar forms that distinguish themselves primarily by the order of a rotation and an anisotropic scaling operation. APnP then boils down to one unique polynomial problem, which is solved by the Grobner basis approach. Experimental results on both simulated and real datasets demonstrate the effectiveness of APnP as a more flexible and practical solution to camera pose estimation.