LiDAR Data Processing for Digitization of the Castro of Santa Trega and Integration in Unreal Engine 5

ABSTRACTWith the advancement of graphic engines, real-life structures can be digitized with more realistic representations than before. Virtual models obtained from LiDAR (Light Detection and Ranging) data in real-time applications can be inspected in graphic engines without rendering a point cloud. Well-known proprietary software is used to convert scanning from LiDAR into meshes of triangles that work the best on graphic pipelines. However proprietary software is usually expensive, hard to learn, and requires manual interaction. The proposed methodology generates virtual models from LiDAR with little manual interaction employing open-source software in an automated workflow for generic conversion. The point cloud is registered for geo-reference, processed for building textured models, and implemented in Unreal Engine 5 for Virtual Reality deployment. Specific improvements were made to the selected study case of the Castro of Santa Trega. Visualization of the model is overall more realistic than the rendering of every point in a cloud. The average framerate is improved upon a 229% when rendering optimized meshes compared to point clouds, leading to an enriched visualization quality and reduced data size. A Virtual Reality (VR) experience was implemented with an average of 143 FPS, surpassing the standard 90 FPS recommended to avoid motion sickness.KEYWORDS: Computer graphicscomputer visionheritagelidarpoint cloud processing AcknowledgmentsThis research has received funding from Xunta de Galicia through human resources grant (ED481D-2023-005) and competitive reference group (ED431C 2020/01) and from the Government of Spain through project “Software multi-capa para el procesamiento online de datos lidar enfocado a la monitorización del transporte” with reference PDC2022-133851-I00 funded by MCIN/AEI/10.13039/501100011033, and from the European Union’s “NextGenerationEU”/PRTR”. This work has been partially supported by Grant RYC2021–033560-I funded by MCIN/AEI/10.13039/501100011033 and by European Union NextGenerationEU/PRTR. This paper was carried out in the framework of the InfraROB project (Maintaining integrity, performance and safety of the road infrastructure through autonomous robotized solutions and modularization), which has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement no. 955337. It reflects only the authors’ views. Neither the European Climate, Infrastructure, and Environment Executive Agency (CINEA) nor the European Commission is in any way responsible for any use that may be made of the information it contains.Disclosure statementNo potential conflict of interest was reported by the authors.Additional informationFundingThe work was supported by the Horizon 2020 Framework Programme Gobierno de España Xunta de Galicia .