Data Fusion of Objects Using Techniques Such as Laser Scanning, Structured Light and Photogrammetry for Cultural Heritage Applications

TL;DR

This paper introduces a semi-automatic pipeline for accurately coloring 3D models from scans using uncalibrated images, combining SfM, coarse registration, and local refinement to improve visual quality in cultural heritage applications.

Contribution

It presents a novel semi-automatic registration pipeline that integrates SfM, SICP, and local refinement for enhanced 3D model coloring from uncalibrated images.

Findings

Effective registration across diverse object complexities

Reduces artifacts like blurring and ghosting

Handles real-world cultural heritage objects

Abstract

In this paper we present a semi-automatic 2D-3D local registration pipeline capable of coloring 3D models obtained from 3D scanners by using uncalibrated images. The proposed pipeline exploits the Structure from Motion (SfM) technique in order to reconstruct a sparse representation of the 3D object and obtain the camera parameters from image feature matches. We then coarsely register the reconstructed 3D model to the scanned one through the Scale Iterative Closest Point (SICP) algorithm. SICP provides the global scale, rotation and translation parameters, using minimal manual user intervention. In the final processing stage, a local registration refinement algorithm optimizes the color projection of the aligned photos on the 3D object removing the blurring/ghosting artefacts introduced due to small inaccuracies during the registration. The proposed pipeline is capable of handling real world cases with a range of characteristics from objects with low level geometric features to complex ones.