Images from our projects.

Show images of current Projects | Years: 2024 - 2025 - 2026.

Advanced Computational Design

Instant Visualization and Interaction for Large Point Clouds

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.


Modeling the World at Scale

Vision: reconstruct a model of the world that permits online level-of-detail extraction.

Massive geographische Datenvisualisierung mit WebGPU

Geographische Daten, wie etwa Bewegungsdaten oder geolokalisierte Messungen über die Zeit, sind oft mehrere Gigabyte groß und können daher nicht mehr mit klassischen online Tools analysiert und präsentiert werden. Wir wollen helfen, Datenwissenschaftler_innen, Datenjournalist_innen und der breiten Masse diese Daten durch interaktive Echtzeitvisualisierung im Web zugänglich machen.

 

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