57 citations · 123 across the 13 of their papers we have counts for
13 papers
Navigating Perplexity: A linear relationship with the data set size in t-SNE embeddings
Martin Skrodzki, Nicolas F. Chaves-de-Plaza, Thomas Höllt +2
Widely used pipelines for analyzing high-dimensional data utilize two-dimensional visualizations. These are created, for instance, via t-distributed stochastic neighbor embedding (…
A Fast Geometric Multigrid Method for Curved Surfaces
Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann +1
We introduce a geometric multigrid method for solving linear systems arising from variational problems on surfaces in geometry processing, Gravo MG. Our scheme uses point clouds as…
Parametrizing Product Shape Manifolds by Composite Networks
Josua Sassen, Klaus Hildebrandt, Martin Rumpf +1
Parametrizations of data manifolds in shape spaces can be computed using the rich toolbox of Riemannian geometry. This, however, often comes with high computational costs, which ra…
ProtoFold Neighborhood Inspector
Nicolas F. Chaves-de-Plaza, Klaus Hildebrandt, Anna Vilanova
Post-translational modifications (PTMs) affecting a protein's residues (amino acids) can disturb its function, leading to illness. Whether or not a PTM is pathogenic depends on its…
Report on AI-Infused Contouring Workflows for Adaptive Proton Therapy in the Head and Neck
Nicolas F. Chaves-de-Plaza, Prerak Mody, Klaus Hildebrandt +5
Delineation of tumors and organs-at-risk permits detecting and correcting changes in the patients' anatomy throughout the treatment, making it a core step of adaptive proton therap…
Deep vanishing point detection: Geometric priors make dataset variations vanish
Yancong Lin, Ruben Wiersma, Silvia L. Pintea +3
Deep learning has improved vanishing point detection in images. Yet, deep networks require expensive annotated datasets trained on costly hardware and do not generalize to even sli…
Evaluations of The Hierarchical Subspace Iteration Method
Ahmad Nasikun, Klaus Hildebrandt
This document contains additional experiments concerned with the evaluation of the Hierarchical Subspace Iteration Method, which is introduced in~\cite{Nasikun2021}}
The Hierarchical Subspace Iteration Method for Laplace--Beltrami Eigenproblems
Ahmad Nasikun, Klaus Hildebrandt
Sparse eigenproblems are important for various applications in computer graphics. The spectrum and eigenfunctions of the Laplace--Beltrami operator, for example, are fundamental fo…
DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds
Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann +1
Learning from 3D point-cloud data has rapidly gained momentum, motivated by the success of deep learning on images and the increased availability of 3D~data. In this paper, we aim…
Comparing Bayesian Models for Organ Contouring in Head and Neck Radiotherapy
Prerak Mody, Nicolas Chaves-de-Plaza, Klaus Hildebrandt +3
Deep learning models for organ contouring in radiotherapy are poised for clinical usage, but currently, there exist few tools for automated quality assessment (QA) of the predicted…
CNNs on Surfaces using Rotation-Equivariant Features
Ruben Wiersma, Elmar Eisemann, Klaus Hildebrandt
This paper is concerned with a fundamental problem in geometric deep learning that arises in the construction of convolutional neural networks on surfaces. Due to curvature, the tr…
Geometric optimization using nonlinear rotation-invariant coordinates
Josua Sassen, Behrend Heeren, Klaus Hildebrandt +1
Geometric optimization problems are at the core of many applications in geometry processing. The choice of a representation fitting an optimization problem can considerably simplif…
Proofs of two Theorems concerning Sparse Spacetime Constraints
Christian Schulz, Christoph von Tycowicz, Hans-Peter Seidel +1
In the SIGGRAPH 2014 paper [SvTSH14] an approach for animating deformable objects using sparse spacetime constraints is introduced. This report contains the proofs of two theorems…