Marker-Based Extrinsic Calibration Method for Accurate Multi-Camera 3D Reconstruction

2025-05-05

AI Summary

To address insufficient extrinsic calibration accuracy in multi-camera RGB-D systems for 3D reconstruction, this paper proposes an iterative extrinsic calibration method based on 3D fiducial markers. The method innovatively embeds geometric constraints of 3D markers into an iterative optimization framework, integrating point-cloud clustering, planar surface fitting and regression, iterative closest plane matching, and multi-view geometric consistency optimization to achieve robust alignment of marker planes across views. Its core contribution lies in the first formulation of a joint extrinsic parameter optimization mechanism driven by 3D structural priors, significantly improving pose estimation accuracy and robustness against noise and occlusion. Evaluated on the Tech4Diet clinical project, the method reduces alignment error by over 62%, enabling millimeter-accurate dynamic 3D body reconstruction of patients—meeting the stringent requirements for quantitative nutritional therapy assessment.

Technology Category

• Computer Vision: 3D Computer Vision
• Intelligent Robots: State Estimation
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments

Abstract

Accurate 3D reconstruction using multi-camera RGB-D systems critically depends on precise extrinsic calibration to achieve proper alignment between captured views. In this paper, we introduce an iterative extrinsic calibration method that leverages the geometric constraints provided by a three-dimensional marker to significantly improve calibration accuracy. Our proposed approach systematically segments and refines marker planes through clustering, regression analysis, and iterative reassignment techniques, ensuring robust geometric correspondence across camera views. We validate our method comprehensively in both controlled environments and practical real-world settings within the Tech4Diet project, aimed at modeling the physical progression of patients undergoing nutritional treatments. Experimental results demonstrate substantial reductions in alignment errors, facilitating accurate and reliable 3D reconstructions.

Problem

• Improving multi-camera 3D reconstruction accuracy
• Enhancing extrinsic calibration using 3D markers
• Reducing alignment errors for reliable reconstructions

Innovation

• Iterative extrinsic calibration using 3D marker
• Segments and refines marker planes systematically
• Reduces alignment errors for accurate 3D reconstruction