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Large-Scale Urban Reconstruction with Tensor Clustering and Global Boundary Refinement
IEEE Transactions on Pattern Analysis and Machine Intelligence ( IF 23.6 ) Pub Date : 2019-01-18 , DOI: 10.1109/tpami.2019.2893671
Charalambos Poullis

Accurate and efficient methods for large-scale urban reconstruction are of significant importance to the computer vision and computer graphics communities. Although rapid acquisition techniques such as airborne LiDAR have been around for many years, creating a useful and functional virtual environment from such data remains difficult and labor intensive. This is due largely to the necessity in present solutions for data dependent user defined parameters. In this paper we present a new solution for automatically converting large LiDAR data pointcloud into simplified polygonal 3D models. The data is first divided into smaller components which are processed independently and concurrently to extract various metrics about the points. Next, the extracted information is converted into tensors. A robust agglomerate clustering algorithm is proposed to segment the tensors into clusters representing geospatial objects e.g., roads, buildings, etc. Unlike previous methods, the proposed tensor clustering process has no data dependencies and does not require any user-defined parameter. The required parameters are adaptively computed assuming a Weibull distribution for similarity distances. Lastly, to extract boundaries from the clusters a new multi-stage boundary refinement process is developed by reformulating this extraction as a global optimization problem. We have extensively tested our methods on several pointcloud datasets of different resolutions which exhibit significant variability in geospatial characteristics e.g., ground surface inclination, building density, etc and the results are reported. The source code for both tensor clustering and global boundary refinement will be made publicly available with the publication on the author's website.

中文翻译:

张量聚类和全球边界细化的大规模城市改造

准确有效的大规模城市重建方法对于计算机视觉和计算机图形学界至关重要。尽管诸如机载LiDAR之类的快速采集技术已经存在了很多年,但是从此类数据创建有用的功能性虚拟环境仍然十分困难且劳动强度大。这主要是由于当前解决方案中依赖于数据的用户定义参数的必要性。在本文中,我们提出了一种新的解决方案,可将大型LiDAR数据点云自动转换为简化的多边形3D模型。首先将数据分为较小的组件,这些组件将独立并同时进行处理,以提取有关点的各种度量。接下来,将提取的信息转换为张量。提出了一种鲁棒的聚集聚类算法,将张量分割成代表地理空间对象(如道路,建筑物等)的聚类。与以前的方法不同,所提出的张量聚类过程没有数据依赖性,并且不需要任何用户定义的参数。假定相似距离的Weibull分布,自适应计算所需参数。最后,为了从聚类中提取边界,通过将提取重新配置为全局优化问题,开发了一种新的多阶段边界细化方法。我们已经在几个不同分辨率的点云数据集上对我们的方法进行了广泛的测试,这些数据集在地理空间特征(例如地表倾斜度,建筑密度等)方面表现出很大的可变性,并报告了结果。
更新日期:2020-04-22
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