Research on 3D Reconstruction Methods for Incomplete Building Point Clouds Using Deep Learning and Geometric Primitives
文献类型：期刊论文
| 作者 | Ding, Ziqi6; Lu, Yuefeng4,5,6; Shao, Shiwei2,3; Qin, Yong6; Lu, Miao1,4; Song, Zhenqi6; Sun, Dengkuo6 |
| 刊名 | REMOTE SENSING
 |
| 出版日期 | 2025-02-01 |
| 卷号 | 17期号:3页码:399 |
| 关键词 | three-dimensional reconstruction point cloud processing deep learning point cloud completion |
| DOI | 10.3390/rs17030399 |
| 产权排序 | 2 |
| 文献子类 | Article |
| 英文摘要 | Point cloud data, known for their accuracy and ease of acquisition, are commonly used for reconstructing level of detail 2 (LoD-2) building models. However, factors like object occlusion can cause incompleteness, negatively impacting the reconstruction process. To address this challenge, this paper proposes a method for reconstructing LoD-2 building models from incomplete point clouds. We design a generative adversarial network model that incorporates geometric constraints. The generator utilizes a multilayer perceptron with a curvature attention mechanism to extract multi-resolution features from the input data and then generates the missing portions of the point cloud through fully connected layers. The discriminator iteratively refines the generator's predictions using a loss function that is combined with plane-aware Chamfer distance. For model reconstruction, the proposed method extracts a set of candidate polygons from the point cloud and computes weights for each candidate polygon based on a weighted energy term tailored to building characteristics. The most suitable planes are retained to construct the LoD-2 building model. The performance of this method is validated through extensive comparisons with existing state-of-the-art methods, showing a 10.9% reduction in the fitting error of the reconstructed models, and real-world data are tested to evaluate the effectiveness of the method. |
| URL标识 | 查看原文 |
| WOS关键词 | ALGORITHM |
| WOS研究方向 | Environmental Sciences & Ecology ; Geology ; Remote Sensing ; Imaging Science & Photographic Technology |
| 语种 | 英语 |
| WOS记录号 | WOS:001419594900001 |
| 出版者 | MDPI |
| 源URL | [http://ir.igsnrr.ac.cn/handle/311030/212349]  |
| 专题 | 资源与环境信息系统国家重点实验室_外文论文 |
| 通讯作者 | Lu, Yuefeng |
| 作者单位 | 1.Chinese Acad Agr Sci, Inst Agr Resources & Reg Planning, State Key Lab Efficient Utilizat Arid & Semiarid A, Beijing 100081, Peoples R China 2.Hubei Engn Res Ctr Intelligent Detect & Identifica, Wuhan 430205, Peoples R China; 3.Wuhan Open Univ, Wuhan Vocat Coll Software & Engn, Wuhan 430205, Peoples R China; 4.Natl Ctr Technol Innovat Comprehens Utilizat Salin, Dongying 257347, Peoples R China; 5.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, State Key Lab Resources & Environm Informat Syst, Beijing 100101, Peoples R China; 6.Shandong Univ Technol, Sch Civil Engn & Geomat, Zibo 255049, Peoples R China; |
| 推荐引用方式 GB/T 7714 | Ding, Ziqi,Lu, Yuefeng,Shao, Shiwei,et al. Research on 3D Reconstruction Methods for Incomplete Building Point Clouds Using Deep Learning and Geometric Primitives[J]. REMOTE SENSING,2025,17(3):399. |
| APA | Ding, Ziqi.,Lu, Yuefeng.,Shao, Shiwei.,Qin, Yong.,Lu, Miao.,...&Sun, Dengkuo.(2025).Research on 3D Reconstruction Methods for Incomplete Building Point Clouds Using Deep Learning and Geometric Primitives.REMOTE SENSING,17(3),399. |
| MLA | Ding, Ziqi,et al."Research on 3D Reconstruction Methods for Incomplete Building Point Clouds Using Deep Learning and Geometric Primitives".REMOTE SENSING 17.3(2025):399. |
入库方式： OAI收割
来源：地理科学与资源研究所
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