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Fast Building Instance Proxy Reconstruction for Large Urban Scenes | |
Guo, Jianwei1![]() | |
发表期刊 | IEEE Transactions on Pattern Analysis and Machine Intelligence
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ISSN | 1939-3539 |
2024-04 | |
卷号 | /期号:/页码:1-17 |
摘要 | Digitalization of large-scale urban scenes (in particular buildings) has been a long-standing open problem, which attributes to the challenges in data acquisition, such as incomplete scene coverage, lack of semantics, low efficiency, and low reliability in path planning. In this paper, we address these challenges in urban building reconstruction from aerial images, and we propose an effective workflow and a few novel algorithms for efficient 3D building instance proxy reconstruction for large urban scenes. Specifically, we propose a novel learning-based approach to instance segmentation of urban buildings from aerial images followed by a voting-based algorithm to fuse the multi-view instance information to a sparse point cloud (reconstructed using a standard Structure from Motion pipeline). Our method enables effective instance segmentation of the building instances from the point cloud. We also introduce a layer-based surface reconstruction method dedicated to the 3D reconstruction of building proxies from extremely sparse point clouds. Extensive experiments on both synthetic and real-world aerial images of large urban scenes have demonstrated the effectiveness of our approach. The generated scene proxy models can already provide a promising 3D surface representation of the buildings in large urban scenes, and when applied to aerial path planning, the instance-enhanced building proxy models can significantly improve data completeness and accuracy, yielding highly detailed 3D building models. |
关键词 | Aerial path planning , instance segmentation , photogrammetry , surface reconstruction , urban scene reconstruction |
学科门类 | 工学::计算机科学与技术(可授工学、理学学位) |
DOI | 10.1109/TPAMI.2024.3388371 |
URL | 查看原文 |
收录类别 | SCIE |
语种 | 英语 |
是否为代表性论文 | 是 |
七大方向——子方向分类 | 计算机图形学与虚拟现实 |
国重实验室规划方向分类 | 视觉信息处理 |
是否有论文关联数据集需要存交 | 否 |
引用统计 | |
文献类型 | 期刊论文 |
条目标识符 | http://ir.ia.ac.cn/handle/173211/57109 |
专题 | 多模态人工智能系统全国重点实验室_三维可视计算 |
通讯作者 | Huang,Hui |
作者单位 | 1.MAIS, Institute of Automation, Chinese Academy of Sciences 2.University of Chinese Academy of Sciences 3.Shenzhen University 4.Guangdong Laboratory of Artificial Intelligence and Digital Economy 5.Delft University of Technology |
第一作者单位 | 中国科学院自动化研究所 |
推荐引用方式 GB/T 7714 | Guo, Jianwei,Qin, Haobo,Zhou, Yinchang,et al. Fast Building Instance Proxy Reconstruction for Large Urban Scenes[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence,2024,/(/):1-17. |
APA | Guo, Jianwei,Qin, Haobo,Zhou, Yinchang,Chen, Xin,Nan, Liangliang,&Huang,Hui.(2024).Fast Building Instance Proxy Reconstruction for Large Urban Scenes.IEEE Transactions on Pattern Analysis and Machine Intelligence,/(/),1-17. |
MLA | Guo, Jianwei,et al."Fast Building Instance Proxy Reconstruction for Large Urban Scenes".IEEE Transactions on Pattern Analysis and Machine Intelligence /./(2024):1-17. |
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文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
2024-TPAMI-Fast Buil(8629KB) | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 |
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