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

arXiv: https://arxiv.org/abs/2505.02539 · HTML: https://mlsanity.com/paper/2505.02539/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 17 references that are in the map.
- First posted on arXiv 5 May 2025.

## Builds on (most cited first, 3 of 3)

- [PeLiCal: Targetless Extrinsic Calibration via Penetrating Lines for RGB-D Cameras with Limited Co-visibility](https://mlsanity.com/paper/2404.13949/) (2024)
- [Three-dimensional planar model estimation using multi-constraint knowledge based on k-means and RANSAC](https://mlsanity.com/paper/1708.01143/) (2017)
- [Neural Real-Time Recalibration for Infrared Multi-Camera Systems](https://mlsanity.com/paper/2410.14505/) (2024)

## Built on by (most cited first, 1 of 1)

- [One target to align them all: LiDAR, RGB and event cameras extrinsic calibration for Autonomous Driving](https://mlsanity.com/paper/2511.12291/) (2025)

## Abstract (arXiv)

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.