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Welcome


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Welcome to the Computer Graphics Group at RWTH Aachen University!

The research and teaching activities at our institute focus on geometry acquisition and processing, on interactive visualization, and on related areas such as computer vision, photo-realistic image synthesis, and ultra high speed multimedia data transmission.

In our projects we are cooperating with various industry companies as well as with academic research groups around the world. Results are published and presented at high-profile conferences and symposia. Additional funding sources, among others, are the Deutsche Forschungsgemeinschaft and the European Union.

News

We have a paper on Anisotropic Quad Mesh Refinement at the Eurographics Symposium on Geometry Processing 2020.

June 26, 2020

We have a paper on Surface Texture Synthesis in the PACMCGIT journal.

May 27, 2020

We have a paper on Inter-Surface Maps at SIGGRAPH 2020.

May 5, 2020

We have a paper on Probabilistic Quadrics at Eurographics 2020.

April 30, 2020

We have a paper on High-Fidelity Point-based Rendering in the IEEE Computer Graphics & Applications Journal.

Feb. 11, 2020

We have a paper on Inter-Surface Maps at SIGGRAPH Asia 2019.

Aug. 26, 2019

Recent Publications

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Inter-Surface Maps via Constant-Curvature Metrics

SIGGRAPH 2020

We propose a novel approach to represent maps between two discrete surfaces of the same genus and to minimize intrinsic mapping distortion. Our maps are well-defined at every surface point and are guaranteed to be continuous bijections (surface homeomorphisms). As a key feature of our approach, only the images of vertices need to be represented explicitly, since the images of all other points (on edges or in faces) are properly defined implicitly. This definition is via unique geodesics in metrics of constant Gaussian curvature. Our method is built upon the fact that such metrics exist on surfaces of arbitrary topology, without the need for any cuts or cones (as asserted by the uniformization theorem). Depending on the surfaces' genus, these metrics exhibit one of the three classical geometries: Euclidean, spherical or hyperbolic. Our formulation handles constructions in all three geometries in a unified way. In addition, by considering not only the vertex images but also the discrete metric as degrees of freedom, our formulation enables us to simultaneously optimize the images of these vertices and images of all other points.

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Fast and Robust QEF Minimization using Probabilistic Quadrics

Computer Graphics Forum (Proc. EUROGRAPHICS 2020)

Error quadrics are a fundamental and powerful building block in many geometry processing algorithms. However, finding the minimizer of a given quadric is in many cases not robust and requires a singular value decomposition or some ad-hoc regularization. While classical error quadrics measure the squared deviation from a set of ground truth planes or polygons, we treat the input data as genuinely uncertain information and embed error quadrics in a probabilistic setting ("probabilistic quadrics") where the optimal point minimizes the expected squared error. We derive closed form solutions for the popular plane and triangle quadrics subject to (spatially varying, anisotropic) Gaussian noise. Probabilistic quadrics can be minimized robustly by solving a simple linear system - 50x faster than SVD. We show that probabilistic quadrics have superior properties in tasks like decimation and isosurface extraction since they favor more uniform triangulations and are more tolerant to noise while still maintaining feature sensitivity. A broad spectrum of applications can directly benefit from our new quadrics as a drop-in replacement which we demonstrate with mesh smoothing via filtered quadrics and non-linear subdivision surfaces.

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High-Fidelity Point-Based Rendering of Large-Scale 3D Scan Datasets

IEEE Computer Graphics and Applications

Digitalization of 3D objects and scenes using modern depth sensors and high-resolution RGB cameras enables the preservation of human cultural artifacts at an unprecedented level of detail. Interactive visualization of these large datasets, however, is challenging without degradation in visual fidelity. A common solution is to fit the dataset into available video memory by downsampling and compression. The achievable reproduction accuracy is thereby limited for interactive scenarios, such as immersive exploration in Virtual Reality (VR). This degradation in visual realism ultimately hinders the effective communication of human cultural knowledge. This article presents a method to render 3D scan datasets with minimal loss of visual fidelity. A point-based rendering approach visualizes scan data as a dense splat cloud. For improved surface approximation of thin and sparsely sampled objects, we propose oriented 3D ellipsoids as rendering primitives. To render massive texture datasets, we present a virtual texturing system that dynamically loads required image data. It is paired with a single-pass page prediction method that minimizes visible texturing artifacts. Our system renders a challenging dataset in the order of 70 million points and a texture size of 1.2 terabytes consistently at 90 frames per second in stereoscopic VR.

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