Efficacy of AI for Three-Dimensional Point Cloud Semantic Segmentation of Heritage Data for XR Environments

Title

Efficacy of AI for Three-Dimensional Point Cloud Semantic Segmentation of Heritage Data for XR Environments

Creator

Subhadha, B.; Jaganathan, Siva

Description

In heritage documentation, three-dimensional (3D) models created using Scan-to-BIM processes are essential for interpreting and presenting historic structures. Point cloud data derived from 3D laser scanning and photogrammetry facilitate realistic digital models used for immersive experiences. For this, raw point clouds, which are unstructured, are processed, semantically classified, and segmented to create parametric architectural objects in modeling platforms. Three-Dimensional Point Cloud Semantic Segmentation (3DPCSS) refers to segmenting point clouds into classes like walls, columns, etc. Automating 3DPCSS using Artificial Intelligence (AI) has gained importance in current research activities because of its versatility and efficiency over manual segmentation. However, implementing it solely with AI presents various operational and conceptual challenges, particularly for XR models in digital heritage. Automated segmentation often fails to capture the unique characteristics and intricate geometries, leading to misrepresentations or oversimplifications. Selecting an appropriate algorithmic framework for automating 3DPCSS is essential to address this gap. This paper aims to understand the efficacy of AI algorithms in recent research for 3DPCSS, particularly those tailored for 3D modeling. A study of Dwarakadesh Haveli, Ahmedabad, India, highlights the workflow and challenges of integrating point clouds into 3D models. The findings indicate the need for a detailed approach tailored to the projects specific characteristics, emphasizing the importance of systematic algorithm ensemble experimentation to refine segmentation, leading to the development of 3D parametric objects. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

Source

Lecture Notes in Civil Engineering;Volume;683 LNCE;pp.67-84

Date

01-01-2025

Publisher

Springer Science and Business Media Deutschland GmbH

Subject

3D Point Cloud Semantic Segmentation (3DPSS); AI in cultural heritage; Digital heritage; Immersive experience; Scan-to-BIM; XR in heritage conservation

Coverage

Subhadha B., Christ University, Bengaluru, India, Communication, Arch + The Arts, Florida International University, Miami, United States; Jaganathan S., Christ University, Bengaluru, India

Rights

Restricted Access; Hardcopy may be available in the library

Relation

ISSN: 23662557; ISBN: 978-981968760-2;

Format

online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-981-96-8761-9_5

https://www.scopus.com/pages/publications/105022165240?origin=resultslist