Autocalibration with the Minimum Number of Cameras with Known Pixel Shape

TL;DR

This paper presents a minimal autocalibration algorithm requiring only five cameras with known pixel shape, significantly reducing the number of cameras needed compared to previous methods, and introduces the six-line conic variety as a key geometric tool.

Contribution

It introduces a new autocalibration method that uses only five cameras and the six-line conic variety, improving efficiency and reducing camera requirements.

Findings

Requires only 5 cameras, the theoretical minimum.

Reduces the search space from 3D to 2D for solutions.

Demonstrates good performance on real images.

Abstract

In 3D reconstruction, the recovery of the calibration parameters of the cameras is paramount since it provides metric information about the observed scene, e.g., measures of angles and ratios of distances. Autocalibration enables the estimation of the camera parameters without using a calibration device, but by enforcing simple constraints on the camera parameters. In the absence of information about the internal camera parameters such as the focal length and the principal point, the knowledge of the camera pixel shape is usually the only available constraint. Given a projective reconstruction of a rigid scene, we address the problem of the autocalibration of a minimal set of cameras with known pixel shape and otherwise arbitrarily varying intrinsic and extrinsic parameters. We propose an algorithm that only requires 5 cameras (the theoretical minimum), thus halving the number of cameras required by previous algorithms based on the same constraint. To this purpose, we introduce as our basic geometric tool the six-line conic variety (SLCV), consisting in the set of planes intersecting six given lines of 3D space in points of a conic. We show that the set of solutions of the Euclidean upgrading problem for three cameras with known pixel shape can be parameterized in a computationally efficient way. This parameterization is then used to solve autocalibration from five or more cameras, reducing the three-dimensional search space to a two-dimensional one. We provide experiments with real images showing the good performance of the technique.