Architectural Co-LOD Generation

Managing the level-of-detail (LOD) in architectural models is crucial yet challenging, particularly for effective representation and visualization of buildings. Traditional approaches often fail to deliver controllable detail alongside semantic consistency, especially when dealing with noisy and inconsistent inputs. We address these limitations with Co-LOD , a new approach specifically designed for effective LOD management in architectural modeling. Co-LOD employs shape co-analysis to standardize geometric structures across multiple buildings, facilitating the progressive and consistent generation of LODs. This method allows for precise detailing in both individual models and model collections, ensuring semantic integrity. Extensive experiments demonstrate that Co-LOD effectively applies accurate LOD across a variety of architectural inputs, consistently delivering superior detail and quality in LOD representations.

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Publication Details

Journal
ACM Transactions on Graphics
Published
2024-11-19
DOI
https://doi.org/10.1145/3687905
Citations
4
Primary Topic
3D Surveying and Cultural Heritage
Type
article
Field-Weighted Citation Impact
3.34

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article

Architectural Co-LOD Generation

Chenlei Lv, Hui Huang, Shanshan Pan, Minglun Gong et al.
4 citations
ACM Transactions on Graphics
3D Surveying and Cultural Heritage
3.34
article

Architectural Co-LOD Generation

Chenlei Lv, Hui Huang, Shanshan Pan, Minglun Gong, Runze Zhang
article en
4 citations

Abstract

Managing the level-of-detail (LOD) in architectural models is crucial yet challenging, particularly for effective representation and visualization of buildings. Traditional approaches often fail to deliver controllable detail alongside semantic consistency, especially when dealing with noisy and inconsistent inputs. We address these limitations with Co-LOD , a new approach specifically designed for effective LOD management in architectural modeling. Co-LOD employs shape co-analysis to standardize geometric structures across multiple buildings, facilitating the progressive and consistent generation of LODs. This method allows for precise detailing in both individual models and model collections, ensuring semantic integrity. Extensive experiments demonstrate that Co-LOD effectively applies accurate LOD across a variety of architectural inputs, consistently delivering superior detail and quality in LOD representations.

ACM Transactions on GraphicsVol. 43(6)
Shenzhen University (CN), University of Guelph (CA)
National Natural Science Foundation of China, Basic and Applied Basic Research Foundation of Guangdong Province
Sustainable cities and communities
Openalex Percentile: Top 8%
3D Surveying and Cultural Heritage
3.34
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Architectural Co-LOD Generation — Chenlei Lv, Hui Huang, et al. · ACM Transactions on Graphics (2024) | TGRS Research Map | TGRS