<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at <ext-link ext-link-type="uri" xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="https://github.com/SATOffice/improved_scanner3D">https://github.com/SATOffice/improved_scanner3D</ext-link>.</p>
</abstract>

Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive equipment. Competitive mobile solutions (handheld scanners, LiDARs on vehicles, etc.) do not allow for an accurate and fast mapping of the surface of the scanned object. The article proposes an end-to-end automated solution that enables the use of widely available mobile and stationary scanners. The related system generates a full 3D model of the object based on multiple depth sensors. For this purpose, the scanned object is marked with markers. Markers type and positions are automatically detected and mapped to a template mesh. The reference template is automatically selected for the scanned object, which is then transformed according to the data from the scanners with non-rigid transformation. The solution allows for the fast scanning of complex and varied size objects, constituting a set of training data for segmentation and classification systems of 3D scenes. The main advantage of the proposed solution is its efficiency, which enables real-time scanning and the ability to generate a mesh with a regular structure. It is critical for training data for machine learning algorithms. The source code is available at https://github.com/SATOffice/improved_scanner3D.

A Novel Method for Fast Generation of 3D Objects from Multiple Depth Sensors

PhD Student AGH University of Science and Technology

Journal of Artificial Intelligence and Soft Computing Research

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

{"article-title":"A Novel Method for Fast Generation of 3D Objects from Multiple Depth Sensors"}

Scanning real 3D objects face many technical challenges. Stationary solutions allow for accurate scanning. However, they usually require special and expensive...

A Novel Method for Fast Generation of 3D Objects from Multiple Depth Sensors

Published Online: Mar 11, 2023

Page range: 95 - 105

Received: Nov 11, 2022

Accepted: Feb 14, 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0009

Keywords
3D scan, LiDAR, point cloud, mesh, registration

© 2023 Tomasz Szmuc et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

A Novel Method for Fast Generation of 3D Objects from Multiple Depth Sensors

Published Online: Mar 11, 2023

Page range: 95 - 105

Received: Nov 11, 2022

Accepted: Feb 14, 2023

DOI: https://doi.org/10.2478/jaiscr-2023-0009

Keywords3D scan, LiDAR, point cloud, mesh, registration

© 2023 Tomasz Szmuc et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

Keywords
3D scan, LiDAR, point cloud, mesh, registration