Schütz, MarkusMandlburger, GottfriedOtepka, JohannesWimmer, MichaelPanozzo, Daniele and Assarsson, Ulf2020-05-242020-05-2420201467-8659https://doi.org/10.1111/cgf.13911https://diglib.eg.org:443/handle/10.1111/cgf13911Research in rendering large point clouds traditionally focused on the generation and use of hierarchical acceleration structures that allow systems to load and render the smallest fraction of the data with the largest impact on the output. The generation of these structures is slow and time consuming, however, and therefore ill-suited for tasks such as quickly looking at scan data stored in widely used unstructured file formats, or to immediately display the results of point-cloud processing tasks. We propose a progressive method that is capable of rendering any point cloud that fits in GPU memory in real time, without the need to generate hierarchical acceleration structures in advance. Our method supports data sets with a large amount of attributes per point, achieves a load performance of up to 100 million points per second, displays already loaded data in real time while remaining data is still being loaded, and is capable of rendering up to one billion points using an on-the-fly generated shuffled vertex buffer as its data structure, instead of slow-to-generate hierarchical structures. Shuffling is done during loading in order to allow efficiently filling holes with random subsets, which leads to a higher quality convergence behavior.Attribution 4.0 International LicenseComputing methodologiesRenderingRasterization10.1111/cgf.1391151-64