Organization and Query of Point Clouds Data Based on SQL Server Spatial
被引:1
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作者:
Chen, Yijin
论文数: 0引用数: 0
h-index: 0
机构:
China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R ChinaChina Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R China
Chen, Yijin
[1
]
Zhang, Huixia
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h-index: 0
机构:
China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R ChinaChina Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R China
Zhang, Huixia
[1
]
Fu, Xiaoxue
论文数: 0引用数: 0
h-index: 0
机构:
China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R ChinaChina Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R China
Fu, Xiaoxue
[1
]
机构:
[1] China Univ Min & Technol, Coll Geosci & Surveying Engn, Beijing, Beijing, Peoples R China
来源:
PROCEEDINGS OF 2010 3RD IEEE INTERNATIONAL CONFERENCE ON COMPUTER SCIENCE AND INFORMATION TECHNOLOGY (ICCSIT 2010), VOL 7
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2010年
关键词:
point cloudss data;
SQL Server 2008 Spatial;
index;
D O I:
10.1109/ICCSIT.2010.5564615
中图分类号:
TP301 [理论、方法];
学科分类号:
081202 ;
摘要:
Three-dimensional laser scanning produces a large number of point clouds data. In order to efficiently retrieve the point clouds data, this paper stores the point clouds data in SQL Server 2008, and create a spatial index. Using C # software and SQL Server 2008 Spatial technology to achieve the storage and inquiry of point clouds data, and the results of the inquiry is carried out visualization in the MapObjects component, preparing for the follow-up feature extraction. The test shows that the method greatly improves the efficiency of point clouds data retrieval.