测绘通报 ›› 2025, Vol. 0 ›› Issue (5): 84-88.doi: 10.13474/j.cnki.11-2246.2025.0514

• 学术研究 • 上一篇    下一篇

基于投影和特征激活的实景三维玻璃幕墙分割

刘松焕1, 李朝奎1, 田沁2, 江岭3   

  1. 1. 湖南科技大学地理空间信息技术国家地方联合工程实验室, 湖南 湘潭 411201;
    2. 自然资源部城市国土资源监测与仿真重点实验室, 广东 深圳 518034;
    3. 滁州学院, 安徽 滁州 239099
  • 收稿日期:2024-09-14 发布日期:2025-06-05
  • 通讯作者: 李朝奎。E-mail:chkl_@163.com
  • 作者简介:刘松焕(1999—),女,硕士生,研究方向为实景三维建模与单体化。E-mail:837146033@qq.com
  • 基金资助:
    国家自然科学基金(42171418);自然资源部城市国土资源监测与仿真重点实验室开放课题(KF-2023-08-09);湖南省自然科学基金(2024JJ8328);安徽省重点实验室开放基金(2024PGE001);湖南省自然资源科技计划(20230122CH);实景三维建设与应用技术湖南省工程研究中心开放课题(3DRS2024H3)

Real-scene 3D glass curtain wall segmentation method based on projection and feature activation

LIU Songhuan1, LI Chaokui1, TIAN Qin2, JIANG Ling3   

  1. 1. National and Local Joint Engineering Laboratory of Geospatial Information Technology, Hunan University of Science and Technology, Xiangtan 411201, China;
    2. Key Laboratory of Urban Land and Resources Monitoring and Simulation, Ministry of Natural Resources, Shenzhen 518034, China;
    3. Chuzhou University, Chuzhou 239099, China
  • Received:2024-09-14 Published:2025-06-05

摘要: 针对城市实景三维模型易出现玻璃幕墙模糊、扭曲等问题,本文提出了一种基于投影和特征激活的实景三维玻璃幕墙分割方法。该方法首先利用点云地物特征分层提取建筑物立面信息;然后使用基于投影的分割方法将玻璃立面投影到航拍图像上;最后在U-Net网络结构上嵌套特征激活模块,对玻璃幕墙实现精确提取。将本文方法与U-Net和U2-Net模型进行了对比,本文方法不仅能够提取清晰的玻璃区域边界,而且可有效减轻倾斜航拍图像对小玻璃区域的影响,提升分割性能,对实现低成本精细化建模具有重要意义。

关键词: 实景三维, 玻璃幕墙提取, 投影分割, 卷积神经网络

Abstract: Aiming at the problems of blur and distortion of glass curtain wall in urban 3D model, this paper proposes a 3D glass curtain wall segmentation method based on projection and feature activation. The method firstly extracts building facade information by layers using point cloud feature. Then the glass facade is projected onto the aerial image using a projection based segmentation method. Finally, the feature activation module is nested on U-Net network structure to achieve accurate extraction of glass curtain wall. By comparing the proposed method with U-Net and U2-Net models, the new method proposed in this paper can not only extract clear glass region boundaries, but also effectively reduce the influence of oblique aerial images on small glass regions and improve segmentation performance, which is of great significance for realizing low-cost fine modeling.

Key words: 3D real scene, glass curtain wall extraction, partition, convolutional neural network

中图分类号: