A polynomial formula for the perspective four points problem

TL;DR

This paper introduces a novel polynomial formula for solving the perspective four points problem efficiently by reducing it to an absolute orientation problem, resulting in faster and accurate solutions suitable for real-time applications.

Contribution

The authors develop a new approach that separates variables and reduces the perspective 4-point problem to an explicit formula-based absolute orientation problem, improving speed and efficiency.

Findings

Solution is an order of magnitude faster than existing algorithms.

Achieves similar accuracy under realistic noise conditions.

Reduces computational complexity, enabling efficient RANSAC seed rejection.

Abstract

We present a fast and accurate solution to the perspective $n$-points problem, by way of a new approach to the n=4 case. Our solution hinges on a novel separation of variables: given four 3D points and four corresponding 2D points on the camera canvas, we start by finding another set of 3D points, sitting on the rays connecting the camera to the 2D canvas points, so that the six pair-wise distances between these 3D points are as close as possible to the six distances between the original 3D points. This step reduces the perspective problem to an absolute orientation problem, which has a solution via explicit formula. To solve the first problem we set coordinates which are as orientation-free as possible: on the 3D points side our coordinates are the squared distances between the points. On the 2D canvas-points side our coordinates are the dot products of the points after rotating one of them to sit on the optical axis. We then derive the solution with the help of a computer algebra system. Our solution is an order of magnitude faster than state of the art algorithms, while offering similar accuracy under realistic noise. Moreover, our reduction to the absolute orientation problem runs two orders of magnitude faster than other perspective problem solvers, allowing extremely efficient seed rejection when implementing RANSAC.