Virtual Reality

Point clouds are a quintessential 3D geometry representation format, and often the first model obtained from reconstructive efforts, such as LIDAR scans. IVILPC aims for fast, authentic, interactive, and high-quality processing of such point-based data sets. Our project explores high-performance software rendering routines for various point-based primitives, such as point sprites, gaussian splats, surfels, and particle systems. Beyond conventional use cases, point cloud rendering also forms a key component of point-based machine learning methods and novel-view synthesis, where performance is paramount. We will exploit the flexibility and processing power of cutting-edge GPU architecture features to formulate novel, high-performance rendering approaches. The envisioned solutions will be applicable to unstructured point clouds for instant rendering of billions of points. Our research targets minimally-invasive compression, culling methods, and level-of-detail techniques for point-based rendering to deliver high performance and quality on-demand. We explore GPU-accelerated editing of point clouds, as well as common display issues on next-generation display devices. IVILPC lays the foundation for interaction with large point clouds in conventional and immersive environments. Its goal is an efficient data knowledge transfer from sensor to user, with a wide range of use cases to image-based rendering, virtual reality (VR) technology, architecture, the geospatial industry, and cultural heritage.

This research project focuses on 3D motion analysis and motion learning methodologies. We design novel methods for automated analysis of human motion by machine learning. These methods can be applicable in real training scenario or in VR training setup. The results of our motion analysis can help players better understand the errors in their motion and lead to improvement of motion performance. Our motion analysis methods are based on professional knowledge from tennis experts from our partner company VR Motion Learning GmbH & Co KG. We use numerous motion features, including rotations, positions, velocities and others, to analyze the motion.

Our goal is to use virtual reality as scenario for learning correct tennis technique that will be applicable in real tennis game. For this purpose, we plan to join our motion analysis with error visualization techniques in 3D and with novel motion learning methodologies. These methodologies may lead to learning correct sport technique, improvement of performance and prevention of injuries.

This project aims at the creation and real-world deployment of a handheld computer guide for museum visitors, based on Augmented Reality.