57 citations · 201 across the 21 of their papers we have counts for
5 papers · 1 filter
RANRAC: Robust Neural Scene Representations via Random Ray Consensus
Benno Buschmann, Andreea Dogaru, Elmar Eisemann +2
Learning-based scene representations such as neural radiance fields or light field networks, that rely on fitting a scene model to image observations, commonly encounter challenges…
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 (…
ManiVault: A Flexible and Extensible Visual Analytics Framework for High-Dimensional Data
Alexander Vieth, Thomas Kroes, Julian Thijssen +7
Exploration and analysis of high-dimensional data are important tasks in many fields that produce large and complex data, like the financial sector, systems biology, or cultural he…
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…
Template-free Articulated Neural Point Clouds for Reposable View Synthesis
Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
Dynamic Neural Radiance Fields (NeRFs) achieve remarkable visual quality when synthesizing novel views of time-evolving 3D scenes. However, the common reliance on backward deformat…