A Rasterizing Massive LiDAR Points Cloud Algorithm Based on Triangle Driver

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.