Abstract
Abstract. Nowadays, point clouds are the standard product when capturing reality independent of scale and measurement technique. Especially, Dense Image Matching (DIM) and Laser Scanning (LS) are state of the art capturing methods for a great variety of applications producing detailed point clouds up to billions of points. In-depth analysis of such huge point clouds typically requires sophisticated spatial indexing structures to support potentially long-lasting automated non-interactive processing tasks like feature extraction, semantic labelling, surface generation, and the like. Nevertheless, a visual inspection of the point data is often necessary to obtain an impression of the scene, roughly check for completeness, quality, and outlier rates of the captured data in advance. Also intermediate processing results, containing additional per-point computed attributes, may require visual analyses to draw conclusions or to parameterize further processing. Over the last decades a variety of commercial, free, and open source viewers have been developed that can visualise huge point clouds and colorize them based on available attributes. However, they have either a poor loading and navigation performance, visualize only a subset of the points, or require the creation of spatial indexing structures in advance. In this paper, we evaluate a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time without the need of time consuming hierarchical acceleration structure generation. In combination with our multi-threaded LAS and LAZ loaders, we achieve load performance of up to 20 million points per second, display points already while loading, support flexible switching between different attributes, and rendering up to one billion points with visually appealing navigation behaviour. Furthermore, loading times of different data sets for different open source and commercial software packages are analysed.
Highlights
Modern surveying sensors capture the real world with high details producing an enormous amount of data in case of large projects
In addition to LiDAR, 3D point clouds obtained from multi-view stereo via dense image matching (Hirschmuller, 2008), (Haala, Rothermel, 2012) are widely used today, with the clear benefit of inherently providing color information for each matched point
The quality of photogrammetrically derived point clouds is constantly improving considering the ongoing progress in camera technology w.r.t. geometric and radiometric resolution
Summary
Modern surveying sensors capture the real world with high details producing an enormous amount of data in case of large projects. The newest generation of airborne laser scanning systems (e.g. RIEGL VQ-1560 II, Leica TerrainMapper) feature an effective pulse repetition rates of 2 MHz or more. Such systems can measure more than 2 million points per second not even considering multi-target returns. Multi-head cameras with nadir and oblique viewing directions mounted into a single camera frame are becoming state-of-theart. With such sensors, city regions are typically captured with a Ground Sampling Distance (GSD) in the order of 10 cm and
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