Yu Wang
Nanjing University
16 Papers
32 Citations
Yu Wang is an academic researcher from Nanjing University. The author has contributed to research in topics: Point cloud & Medicine. The author has an hindex of 5, co-authored 10 publications.
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Papers
Extraction of Urban Power Lines from Vehicle-Borne LiDAR Data
TL;DR: This paper tries to utilize vehicle-borne LiDAR data for the extraction of urban power lines by proposing a bottom-up method for filtering the power lines belonging to each power line and demonstrating the high precision of this technique.
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Building Point Detection from Vehicle-Borne LiDAR Data Based on Voxel Group and Horizontal Hollow Analysis
TL;DR: The experimental results indicate that the proposed framework for automatic and efficient building point extraction is effective for the extraction of LiDAR points belonging to various types of buildings in large-scale complex urban environments.
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Three-Dimensional Reconstruction of Large Multilayer Interchange Bridge Using Airborne LiDAR Data
TL;DR: A new technical framework based on the structure units for 3-D reconstruction of large multilayer interchange bridge, including point cloud extraction, connectivity-based segmentation, determination of structure units, occlusion detection and restoration, and3-D modeling is proposed.
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Examining Spatio-Temporal Characteristics of Urban Heat Islands and Factors Driving Them in Hangzhou, China
TL;DR: Wang et al. as discussed by the authors analyzed the spatio-temporal characteristics of UHIs in Hangzhou in China in the context of the relationship between nature and humans based on the standard deviational ellipse, Pearson correlation analysis, and principal component analysis.
Patent
Power line extracting and fitting method based on in-vehicle LiDAR data
Cheng Liang,Tong Lihua,Li Manchun,Yu Wang,Yang Wu,Huang Qiuhao,Feixue Li,Chen Yanming,Zhang Wen,Du Peijun +9 more
- 25 Dec 2013
TL;DR: In this paper, a power line extraction and fitting method based on in-vehicle LiDAR data is proposed, which can achieve automatic and quick extraction of a large amount of power line point cloud in the in-Vehicle LiDA data, and also can achieve accurate recognition on the single power line and accurate fitting of three-dimensional models of the power lines.
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