DocumentCode :
3696267
Title :
A Rasterizing Massive LiDAR Points Cloud Algorithm Based on Triangle Driver
Author :
Chunkang Zhang;Xuesheng Zhao
Author_Institution :
Dept. of Remote Sensing, China Univ. of Min. &
Volume :
2
fYear :
2015
Firstpage :
377
Lastpage :
380
Abstract :
It takes much time to do TIN data I/O operation when converting LiDAR points cloud to raster data via TIN. An efficient streaming algorithm based on triangle driver is proposed in this paper. Firstly, a new sub algorithm for converting TIN to GRID based on the straight line positive and negative zone discriminant principle is presented to make the streaming algorithm feasible, for it performs by each triangle rather than by each grid node. Based on the sub algorithm, the streaming algorithm combines constructing triangles, converting triangles to GRID and freeing memory into a pipeline by traversing triangles to simulate the streaming computation. Instead of creating TIN and converting TIN to GRID separately in the CPU, the streaming algorithm integrates them together and creates GRID from points cloud directly. The results show that the sub-algorithm performs efficiently and there is a linear relationship between time consuming and the number of triangles. The streaming algorithm has a better performance in terms of time owing to no TIN data I/O operation and it improves RAM memory utilization. It offers an efficient way to create GRID from a great amount of Li DAR point cloud data and supports parallel computing.
Keywords :
"Algorithm design and analysis","Tin","Three-dimensional displays","Laser radar","Memory management","Computers","Data structures"
Publisher :
ieee
Conference_Titel :
Intelligent Human-Machine Systems and Cybernetics (IHMSC), 2015 7th International Conference on
Print_ISBN :
978-1-4799-8645-3
Type :
conf
DOI :
10.1109/IHMSC.2015.211
Filename :
7334992
Link To Document :
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