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Filling holes in LoD2 building models.

Authors :
Gao, Weixiao
Peters, Ravi
Ledoux, Hugo
Stoter, Jantien
Source :
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences; 2024, Vol. 10 Issue 4/W5, p171-177, 7p
Publication Year :
2024

Abstract

This paper presents a new algorithm for filling holes in Level of Detail 2 (LoD2) building mesh models, addressing the challenges posed by geometric inaccuracies and topological errors. Unlike traditional methods that often alter the original geometric structure or impose stringent input requirements, our approach preserves the integrity of the original model while effectively managing a range of topological errors. The algorithm operates in three distinct phases: (1) pre-processing, which addresses topological errors and identifies pseudo-holes; (2) detecting and extracting complete border rings of holes; and (3) remeshing, aimed at reconstructing the complete geometric surface. Our method demonstrates superior performance compared to related work in filling holes in building mesh models, achieving both uniform local geometry around the holes and structural completeness. Comparative experiments with established methods demonstrate our algorithm's effectiveness in delivering more complete and geometrically consistent hole-filling results, albeit with a slight trade-off in efficiency. The paper also identifies challenges in handling certain complex scenarios and outlines future directions for research, including the pursuit of a comprehensive repair goal for LoD2 models to achieve watertight 2-manifold models with correctly oriented normals. Our source code is available at <code>https://github.com/tudelft3d/Automatic-Repair-of-LoD2-Building-Models.git</code>. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
21949042
Volume :
10
Issue :
4/W5
Database :
Complementary Index
Journal :
ISPRS Annals of Photogrammetry, Remote Sensing & Spatial Information Sciences
Publication Type :
Academic Journal
Accession number :
178235089
Full Text :
https://doi.org/10.5194/isprs-annals-X-4-W5-2024-171-2024