A Minimal Closed-Form Solution for
Multi-Perspective Pose Estimation
using Points and Lines
Abstract. We propose a minimal solution for pose estimation using
both points and lines for a multi-perspective camera. In this paper, we
treat the multi-perspective camera as a collection of rigidly attached
perspective cameras. These type of imaging devices are useful for several
computer vision applications that require a large coverage such as surveillance, self-driving cars, and motion-capture studios. While prior methods
have considered the cases using solely points or lines, the hybrid case involving both points and lines has not been solved for multi-perspective
cameras. We present the solutions for two cases. In the first case, we are
given 2D to 3D correspondences for two points and one line. In the later
case, we are given 2D to 3D correspondences for one point and two lines.
We show that the solution for the case of two points and one line can be
formulated as a fourth degree equation. This is interesting because we
can get a closed-form solution and thereby achieve high computational
efficiency. The later case involving two lines and one point can be mapped
to an eighth degree equation. We show simulations and real experiments
to demonstrate the advantages and benefits over existing methods