Data and code for "Data-Driven Extrinsic Volumetric Calibration of 3D Galvanometric Lasers and Off-Axis Vision Systems for Free-Form Processing"

This repository contains the datasets and code associated with the article “Data-Driven Extrinsic Volumetric Calibration of 3D Galvanometric Lasers and Off-Axis Vision Systems for Free-Form Processing”. The repository includes the raw 3D point-cloud data acquired during the volumetric calibration experiments, the processed calibration matrices extracted from these acquisitions, and the scripts used to generate the camera-to-laser calibration models. The included code covers the complete calibration workflow, including point-cloud preprocessing, centroid extraction, planar-misalignment compensation, polynomial Ridge regression, artificial neural network training, quantitative inclined-plane validation, residual offset compensation, and qualitative free-form processing demonstrations. The dataset contains the experimental data obtained with the structured-light scanner and the scanning laser profilometer, including calibration acquisitions, validation data, and application-oriented free-form geometries. The provided scripts reproduce the main calibration, model production, quantitative evaluation, and qualitative trajectory-generation procedures described in the article. The material is intended to support reproducibility of the proposed black-box extrinsic volumetric calibration method for registering off-axis 3D vision measurements with a commercial 3D galvanometric laser scanner.