Implicit Multi-Camera System Calibration Using Gaussian Processes

TL;DR

This paper introduces a Gaussian Process-based framework for implicit multi-camera calibration that is data-efficient, handles complex distortions, and provides uncertainty quantification, improving calibration robustness and practicality.

Contribution

The work presents a novel GP regression approach for multi-camera calibration that bypasses explicit parameter estimation and incorporates active learning for data efficiency.

Findings

Uncertainty in 3D predictions is higher near cameras.

Sparse data points in uv-space near cameras affect calibration.

The method outperforms traditional calibration techniques in complex scenarios.

Abstract

This paper proposes a novel framework for implicit multi-camera system calibration utilizing Gaussian Process (GP) regression. Conventional explicit calibration methods are constrained by rigid mathematical models and struggle with complex, non-linear distortions from unconventional optics, while existing neural network-based implicit approaches are typically data-hungry and lack inherent uncertainty quantification (UQ). Our GP-based model directly learns the complex, non-linear mapping from 2D image coordinates across all cameras to a 3D world coordinate, completely bypassing time-consuming estimation of explicit intrinsic and extrinsic parameters. Moreover, the inherent UQ is critical for transforming a simple 3D point prediction into a verifiable 3D measurement, complete with statistically-sound confidence bounds. To further enhance data efficiency and practical deployment, we integrate Active Learning (AL), which intelligently leverages the GP's predictive uncertainty to strategically guide the acquisition of new calibration data. This approach results in a robust, data-efficient, and reliable calibration solution, proving particularly effective in practical scenarios where collecting extensive calibration data is a dominant constraint. Our experiments show that the uncertainty for the 3D predictions is higher closer to the cameras. The data points in $uv$-coordinate space are more sparse in that region, even though they are not in 3D space. This work is relevant for anyone who is tasked with the calibration of complex multi-camera systems.