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Simplifying complex landmark models with holes for 3D maps: a topological perception-based approach.

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Title: Simplifying complex landmark models with holes for 3D maps: a topological perception-based approach.
Authors: Ding, Yuan1,2 (AUTHOR), Chen, Dongming1 (AUTHOR), Zlatanova, Sisi3 (AUTHOR), Wu, Mingguang4 (AUTHOR) wmg@njnu.edu.cn, Cao, Kai5,6 (AUTHOR), Song, Yongze7 (AUTHOR), Yang, Yingbao1 (AUTHOR)
Source: International Journal of Geographical Information Science. Feb2026, Vol. 40 Issue 2, p348-381. 34p.
Subjects: Architectural models, Three-dimensional modeling, Terrain mapping, Shape recognition (Computer vision)
Abstract: Landmarks serve as critical reference points for determining spatial orientations. Owing to the complexity and diversity of the shapes of landmark buildings, numerous fine visual details can hinder the clear identification of three-dimensional (3D) landmark models, posing a challenge for their automatic generation. To address this issue, we propose a method based on topological perception to simplify 3D landmark models, focusing on enhancing global perception features by exaggerating topology-related features. This method involves three key steps: voxelization, hole exaggeration and model generation. We evaluated the effectiveness of exaggeration and conducted a quantitative analysis of its degree of application in landmark buildings. The results demonstrate that topology-based exaggeration significantly improves the perception of 3D landmark models, and the degree of exaggeration is inversely correlated with the proportion of topology-related visual features in the models. Furthermore, a comparative analysis of four commonly used simplification algorithms shows that our method outperforms the other methods across five key evaluation metrics. [ABSTRACT FROM AUTHOR]
Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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DbLabel: Engineering Source
An: 191070883
AccessLevel: 6
PubType: Academic Journal
PubTypeId: academicJournal
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  Data: Simplifying complex landmark models with holes for 3D maps: a topological perception-based approach.
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  Data: <searchLink fieldCode="AR" term="%22Ding%2C+Yuan%22">Ding, Yuan</searchLink><relatesTo>1,2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Chen%2C+Dongming%22">Chen, Dongming</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zlatanova%2C+Sisi%22">Zlatanova, Sisi</searchLink><relatesTo>3</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Wu%2C+Mingguang%22">Wu, Mingguang</searchLink><relatesTo>4</relatesTo> (AUTHOR)<i> wmg@njnu.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Cao%2C+Kai%22">Cao, Kai</searchLink><relatesTo>5,6</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Song%2C+Yongze%22">Song, Yongze</searchLink><relatesTo>7</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Yingbao%22">Yang, Yingbao</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Geographical+Information+Science%22">International Journal of Geographical Information Science</searchLink>. Feb2026, Vol. 40 Issue 2, p348-381. 34p.
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  Data: <searchLink fieldCode="DE" term="%22Architectural+models%22">Architectural models</searchLink><br /><searchLink fieldCode="DE" term="%22Three-dimensional+modeling%22">Three-dimensional modeling</searchLink><br /><searchLink fieldCode="DE" term="%22Terrain+mapping%22">Terrain mapping</searchLink><br /><searchLink fieldCode="DE" term="%22Shape+recognition+%28Computer+vision%29%22">Shape recognition (Computer vision)</searchLink>
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  Data: Landmarks serve as critical reference points for determining spatial orientations. Owing to the complexity and diversity of the shapes of landmark buildings, numerous fine visual details can hinder the clear identification of three-dimensional (3D) landmark models, posing a challenge for their automatic generation. To address this issue, we propose a method based on topological perception to simplify 3D landmark models, focusing on enhancing global perception features by exaggerating topology-related features. This method involves three key steps: voxelization, hole exaggeration and model generation. We evaluated the effectiveness of exaggeration and conducted a quantitative analysis of its degree of application in landmark buildings. The results demonstrate that topology-based exaggeration significantly improves the perception of 3D landmark models, and the degree of exaggeration is inversely correlated with the proportion of topology-related visual features in the models. Furthermore, a comparative analysis of four commonly used simplification algorithms shows that our method outperforms the other methods across five key evaluation metrics. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of International Journal of Geographical Information Science is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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        Value: 10.1080/13658816.2025.2512222
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        Text: English
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        Type: general
      – SubjectFull: Three-dimensional modeling
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      – SubjectFull: Terrain mapping
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      – SubjectFull: Shape recognition (Computer vision)
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            NameFull: Zlatanova, Sisi
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            NameFull: Wu, Mingguang
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              Text: Feb2026
              Type: published
              Y: 2026
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