Development and comparison of aerial photograph aided visualization pipelines for LiDAR datasets

Light Detection and Ranging (LiDAR) technology generates dense and precise three-dimensional datasets in the form of point clouds. Conventional methods of mapping with airborne LiDAR datasets deal with the process of classification or feature specific segmentation. These processes have been observed to be time-consuming and unfit to handle in scenarios where topographic information is required in a small amount of time. Thus there is a requirement of developing methods which process the data and reconstruct the scene in a small amount of time. This paper presents several pipelines for visualizing LiDAR datasets without going through classification and compares them using statistical methods to rank these processes in the order of depth and feature perception. To make the comparison more meaningful, a manually classified and computer-aided design (CAD) reconstructed dataset is also included in the list of compared methods. Results show that a heuristic-based method, previously developed by the authors perform almost equivalent to the manually classified and reconstructed dataset, for the purposes of visualization. This paper makes some distinct contributions as: (1) gives a heuristics-based visualization pipeline for LiDAR datasets, and (2) presents an experimental design supported by statistical analysis to compare different pipelines.