LiDAR Point Cloud Compression for Visualization - Utilizing compression techniques to enable the visualization of a LiDAR point cloud dataset

Abstract

Methods to downsample and compress a large LiDAR point cloud dataset were developed for the purpose of enabling the visualization of a multi-minute sequence in a 3-dimensional OpenGL environment. The reduction in data is the result of segmenting dynamic objects and downsampling the LiDAR point clouds either by sampling a number of points in each cell in a 3-dimensional grid, or by computing an average point for each cell in a finer grid or octree structure. The ground plane can also be downsampled separately, with a different resolution, to give visual distinction between ground and environment. Together with a point cloud compression algorithm the final file size of a point cloud that is usable for visualization purposes can be reduced to 0.06%-3.6% of the original file size depending on chosen resolution.