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Semi-supervised semantic segmentation method for 3 D Mesh building facades based on contrastive learning
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Chun DU, Haowei CHENG, Wenjie ZI*, Hao CHEN, Jun LI
Journal of National Niversity of Defense Technology | 2025, 47(6) : 235 - 244
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Journal of National Niversity of Defense Technology | 2025, 47(6): 235-244
• Control Science and Engineering·Information and Communication Engineering·Electronic Science and Technology •
Semi-supervised semantic segmentation method for 3 D Mesh building facades based on contrastive learning
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Chun DU, Haowei CHENG, Wenjie ZI*, Hao CHEN, Jun LI
Affiliations
  • College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
Published: 2025-12-28 doi: 10.11887/j.issn.1001-2486.24080004
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Semantic segmentation of building facades from 3D mesh data is essential for scene understanding but often relies on costly fine-grained annotations.In response to this issue, a semi-supervised learning approach was proposed, introducing a semi-supervised semantic segmentation method based on contrastive learning SS_CC(semi-supervised semantic segmentation based on contrastive learning and consistency regularization)to segment building facades in 3D mesh data.In the SS_CC method, the enhanced contrastive learning module exploited the class separability between positive and negative samples to more effectively utilize class-specific feature information.Additionally, the proposed feature-space consistency regularization loss improved the discriminative capability of the extracted building facade features by leveraging global feature representations.Experimental results show that the proposed SS_CC method outperforms some mainstream methods in F1 score and mIoU, and has relatively better segmentation performance on building walls and windows.

3D Mesh data  /  building facades  /  contrastive learning  /  semantic segmentation
Chun DU, Haowei CHENG, Wenjie ZI, Hao CHEN, Jun LI. Semi-supervised semantic segmentation method for 3 D Mesh building facades based on contrastive learning[J]. Journal of National Niversity of Defense Technology, 2025 , 47 (6) : 235 -244 . DOI: 10.11887/j.issn.1001-2486.24080004
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Year 2025 volume 47 Issue 6
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doi: 10.11887/j.issn.1001-2486.24080004
  • Receive Date：2024-08-07
  • Online Date：2026-04-16
  • Published：2025-12-28
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  • Received：2024-08-07
Affiliations
    College of Electronic Science and Technology, National University of Defense Technology, Changsha 410073, China
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表12种不同金属材料的力学参数
科
Family		 属数
Number of
genus		 种数
Number of
species		 占总种数比例
Percentage of
total species (%)		 属
Genus		 种数
Number of
species		 占总种数比例
Percentage of total
species (%)
鹅膏菌科Amanitaceae 2 11 5.26 鹅膏菌属 Amanita 10 4.78
小菇科 Mycenaceae 2 12 5.74 丝盖伞属 Inocybe 5 2.39
多孔菌科 Polyporaceae 8 14 6.70 蜡蘑属 Laccaria 5 2.39
红菇科 Russulaceae 3 23 11.00 小皮伞属 Marasmius 6 2.87
小菇属 Mycena 11 5.26
光柄菇属 Pluteus 5 2.39
红菇属 Russula 17 8.13
栓菌属 Trametes 5 2.39
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