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Perspective-$n$-Point (P$n$P) stands as a fundamental algorithm for poseestimation in various applications. In this paper, we present a new approach tothe P$n$P problem with relaxed constraints, eliminating the need for precise 3Dcoordinates, which is especially suitable for object pose estimation wherecorresponding object models may not be available in practice. Built upon theclassical EP$n$P solver, we refer to it as AEP$n$P due to its ability to handleunknown anisotropic scaling factors in addition to the common 6Dtransformation. Through a few algebraic manipulations and a well-chosen frameof reference, this new problem can be boiled down to a simple linear null-spaceproblem followed by point registration-based identification of a similaritytransformation. Experimental results on both simulated and real datasetsdemonstrate the effectiveness of AEP$n$P as a flexible and practical solutionto object pose estimation. Code: https://github.com/goldoak/AEPnP.

AEP$n$P: A Less-constrained EP$n$P Solver for Pose Estimation with Anisotropic Scaling