Partially calibrated semi-generalized pose from hybrid point correspondences
This work addresses a specific camera pose estimation problem in computer vision, offering incremental improvements for scenarios with noisy 3D data.
The paper tackles the problem of estimating the semi-generalized pose of a partially calibrated camera from hybrid 2D-2D and 2D-3D point correspondences, developing three solvers (H51f, H32f, H13f) that show better noise robustness than absolute pose solvers in synthetic and real data evaluations.
In this paper we study the problem of estimating the semi-generalized pose of a partially calibrated camera, i.e., the pose of a perspective camera with unknown focal length w.r.t. a generalized camera, from a hybrid set of 2D-2D and 2D-3D point correspondences. We study all possible camera configurations within the generalized camera system. To derive practical solvers to previously unsolved challenging configurations, we test different parameterizations as well as different solving strategies based on the state-of-the-art methods for generating efficient polynomial solvers. We evaluate the three most promising solvers, i.e., the H51f solver with five 2D-2D correspondences and one 2D-3D correspondence viewed by the same camera inside generalized camera, the H32f solver with three 2D-2D and two 2D-3D correspondences, and the H13f solver with one 2D-2D and three 2D-3D correspondences, on synthetic and real data. We show that in the presence of noise in the 3D points these solvers provide better estimates than the corresponding absolute pose solvers.