Interactive Visual Analytics for Large-scale Particle Simulations

Particle based model simulations are widely used in scientific visualization. In cosmology, particles are used to simulate the evolution of dark matter in the universe. Clusters of particles (that have special statistical properties) are called halos. From a visualization point of view, halos are clusters of particles, each having a position, mass and velocity in three dimensional space, and they can be represented as point clouds that contain various structures of geometric interest such as filaments, membranes, satellite of points, clusters, and cluster of clusters. The thesis investigates methods for interacting with large scale data-sets represented as point clouds. The work mostly aims at the interactive visualization of cosmological simulation based on large particle systems. The study consists of three components: a) two human factors experiments into the perceptual factors that make it possible to see features in point clouds; b) the design and implementation of a user interface making it possible to rapidly navigate through and visualize features in the point cloud, c) software development and integration to support visualization

Interactive exploration of point clouds

Laser scanning, photogrammetry and other 3D scanning approaches generate data sets comprising millions to trillions of points. Modern GPUs can easily render a few million and up to tens of millions of points in real time, but data sets with hundreds of millions of points and more require acceleration structures to be rendered in real time. In this thesis, we present three contributions to the state of the art with the goal of improving the performance as well as the quality of real-time rendered point clouds. Two of our contributions address the performance of LOD structure generation. State-of-the-art approaches achieve a throughput of up to around 1 million points per second, which requires users to wait minutes even for smaller data sets with a few hundred million points. Our proposed solutions are: A bottom-up LOD generation approach that creates LOD structures up to an order of magnitude faster than previous work, and a progressive rendering approach that is capable of rendering any point cloud that fits in GPU memory in real time, without the need to generate LOD structures at all. The former achieves a throughput of up to 10 million points per second, and the latter is capable of loading point clouds at rates of up to 37 million points per second from an industry-standard point-cloud format (LAS), and up to 100 million points per second if the file format matches the vertex buffer format. Since it does not need LOD structures, the progressive rendering approach can render already loaded points right away while additional points are still being loaded.Our third contribution improves the quality of LOD-based point-cloud rendering by introducing a continuous level-of-detail approach that produces gradual transitions in point density, rather than the characteristic and noticeable blocks from discrete LOD structures. It is mainly targeted towards VR applications, where discrete levels of detail are especially noticeable and disturbing, in a large part due to the popping of chunks of points during motion.10