High performance filtering for big datasets from Airborne Laser Scanning with CUDA technology

Abstract

There are many studies on the problems of processing big datasets provided by Airborne Laser Scanning (ALS). The processing of point clouds is often executed in stages or on the fragments of the measurement set. Therefore, solutions that enable the processing of the entire cloud at the same time in a simple, fast, efficient way are the subject of many researches. In this paper, authors propose to use General-Purpose computation on Graphics Processing Units (GPGPUs) to process the big datasets obtained from ALS. GPGPU handles computation for computer graphics using GPUs (Graphic Processing Units). This study was based on programming model Compute Unified Device Architecture (CUDA), which facilitates the development of applications in GPUs. CUDA programming was used to carry out the filtration based on adaptive TIN model method in the initial stage of the processing of big ALS dataset. Results of the analysis showed that GPGPU can be used for the filtration of ALS point clouds and significantly speeds up calculations for big dataset.

Keywords:

• Point cloud
• Big dataset
• CUDA
• Filtration