VICAN: Very Efficient Calibration Algorithm for Large Camera Networks

TL;DR

VICAN introduces a novel calibration method for large camera networks that leverages dynamic object poses and a custom optimization scheme, improving accuracy and efficiency in challenging environments.

Contribution

The paper presents a new approach that incorporates dynamic object poses into camera calibration, extending traditional pose graph optimization with a tailored primal-dual algorithm.

Findings

Effective in large, complex camera networks

Outperforms traditional methods in accuracy

Runs efficiently on large graphs

Abstract

The precise estimation of camera poses within large camera networks is a foundational problem in computer vision and robotics, with broad applications spanning autonomous navigation, surveillance, and augmented reality. In this paper, we introduce a novel methodology that extends state-of-the-art Pose Graph Optimization (PGO) techniques. Departing from the conventional PGO paradigm, which primarily relies on camera-camera edges, our approach centers on the introduction of a dynamic element - any rigid object free to move in the scene - whose pose can be reliably inferred from a single image. Specifically, we consider the bipartite graph encompassing cameras, object poses evolving dynamically, and camera-object relative transformations at each time step. This shift not only offers a solution to the challenges encountered in directly estimating relative poses between cameras, particularly in adverse environments, but also leverages the inclusion of numerous object poses to ameliorate and integrate errors, resulting in accurate camera pose estimates. Though our framework retains compatibility with traditional PGO solvers, its efficacy benefits from a custom-tailored optimization scheme. To this end, we introduce an iterative primal-dual algorithm, capable of handling large graphs. Empirical benchmarks, conducted on a new dataset of simulated indoor environments, substantiate the efficacy and efficiency of our approach.