Prompting Meaning: Optimizing Prompt Engineering for Architectural Point Cloud Interpretation

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dc.contributor.authorPaolanti, Marinaen_US
dc.contributor.authorMuralikrishna, Nikhilen_US
dc.contributor.authorGorgoglione, Lucreziaen_US
dc.contributor.authorPierdicca, Robertoen_US
dc.contributor.editorCampana, Stefanoen_US
dc.contributor.editorFerdani, Danieleen_US
dc.contributor.editorGraf, Holgeren_US
dc.contributor.editorGuidi, Gabrieleen_US
dc.contributor.editorHegarty, Zackaryen_US
dc.contributor.editorPescarin, Sofiaen_US
dc.contributor.editorRemondino, Fabioen_US
dc.date.accessioned2025-09-05T20:25:54Z
dc.date.available2025-09-05T20:25:54Z
dc.date.issued2025
dc.description.abstractThree-dimensional point cloud visualisation is essential for preserving and analysing built heritage by providing detailed insights into architectural forms and spatial configurations. Although human perception naturally integrates visual, spatial and contextual information, AI systems have yet to match this interpretive ability, particularly about 3D point clouds. This gap in interpretation highlights the need for AI approaches that process 3D data not only geometrically but also semantically. To address this challenge, the 3D.LLM project is exploring how combining point clouds with large language models (LLMs) can improve spatial and linguistic understanding. This paper presents a prompt engineering strategy developed as part of the 3D.LLM project to improve the semantic interpretation of architectural point clouds. By linking spatial attributes to language-based reasoning, LLMs are employed to generate richer and more accurate descriptions of cultural heritage environments. Unlike conventional geometric segmentation approaches, which often fail to capture architectural nuances, this system enables a spatially aware and flexible interpretation of 3D data. To refine the AI outputs and ensure spatial precision, domain-specific benchmarks such as ArCH and Objaverse XL have been employed. Preliminary findings suggest that prompt engineering significantly improves interpretability, descriptive accuracy and contextual depth, outperforming traditional automated methods. Beyond improving accessibility to architectural heritage information, this approach encourages interdisciplinary collaboration by making complex 3D structures more accessible and useful to scholars, conservators and a wider audience.en_US
dc.description.sectionheadersSemantics-driven Interaction with Digitized Heritage
dc.description.seriesinformationDigital Heritage
dc.identifier.doi10.2312/dh.20253166
dc.identifier.isbn978-3-03868-277-6
dc.identifier.pages10 pages
dc.identifier.urihttps://doi.org/10.2312/dh.20253166
dc.identifier.urihttps://diglib.eg.org/handle/10.2312/dh20253166
dc.publisherThe Eurographics Associationen_US
dc.rightsAttribution 4.0 International License
dc.rights.urihttps://creativecommons.org/licenses/by/4.0/
dc.subjectCCS Concepts: Computing methodologies → Artificial intelligence
dc.subjectComputing methodologies → Artificial intelligence
dc.titlePrompting Meaning: Optimizing Prompt Engineering for Architectural Point Cloud Interpretationen_US
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Track 05 – Analysis and Interpretation