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Combining Augmented Reality and 3D Printing to Display Patient Models on a Smartphone

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Fusion of Multimodal Imaging and 3D Digitization Using Photogrammetry.

Roland Ramm¹, Pedro de Dios Cruz¹, Stefan Heist¹

¹Fraunhofer Institute for Applied Optics and Precision Engineering IOF, Albert-Einstein-Str. 7, 07745 Jena, Germany.

Sensors (Basel, Switzerland)

|April 13, 2024

Summary

This summary is machine-generated.

This study presents a novel method for superimposing low-resolution multimodal camera data onto high-resolution 3D models. This data fusion technique achieves sub-pixel accuracy, enhancing 3D digitization capabilities.

Keywords:

3D digitization multi-sensor systems multimodal multimodal image fusion multispectral photogrammetry structure from motion

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Area of Science:

Optics and Photonics
Computer Vision
3D Reconstruction

Background:

Multimodal sensors capture diverse scene characteristics, but integrating their data with high-resolution 3D models is challenging.
Low resolution and image quality of multimodal cameras (hyperspectral, thermal) limit their direct use with detailed 3D data.

Purpose of the Study:

To develop and demonstrate a new method for superimposing multimodal image data onto 3D models generated via multi-view photogrammetry.
To overcome the resolution limitations of multimodal cameras in 3D digitization applications.

Main Methods:

A novel superimposition technique was developed to fuse low-resolution multimodal imagery with high-resolution 3D models.
A laboratory setup integrated a high-resolution photo camera, a thermal camera, and a 12-channel multispectral camera on a calibrated rig.
Multi-view photogrammetry was used to create a detailed 3D model, onto which multimodal data was superimposed.

Main Results:

The data fusion method achieved an accuracy better than one pixel for multimodal superimposition.
Successful demonstration of multimodal 3D digitization using integrated camera systems.

Conclusions:

The proposed method effectively integrates multimodal sensor data with high-resolution 3D models, enhancing information gain.
This approach offers a pathway for advanced 3D digitization with improved material and condition analysis.