no code implementations • 23 Jan 2024 • Martin Skrodzki, Hunter van Geffen, Nicolas F. Chaves-de-Plaza, Thomas Höllt, Elmar Eisemann, Klaus Hildebrandt
The need to understand the structure of hierarchical or high-dimensional data is present in a variety of fields.
no code implementations • 15 Dec 2023 • Benno Buschmann, Andreea Dogaru, Elmar Eisemann, Michael Weinmann, Bernhard Egger
We demonstrate the compatibility and potential of our solution for both photo-realistic robust multi-view reconstruction from real-world images based on neural radiance fields and for single-shot reconstruction based on light-field networks.
no code implementations • 29 Aug 2023 • Martin Skrodzki, Nicolas Chaves-de-Plaza, Klaus Hildebrandt, Thomas Höllt, Elmar Eisemann
Further, we show how this approach speeds up the computation and increases the quality of the embeddings.
1 code implementation • NeurIPS 2023 • Lukas Uzolas, Elmar Eisemann, Petr Kellnhofer
We demonstrate the versatility of our representation on a variety of articulated objects from common datasets and obtain reposable 3D reconstructions without the need of object-specific skeletal templates.
1 code implementation • CVPR 2022 • Yancong Lin, Ruben Wiersma, Silvia L. Pintea, Klaus Hildebrandt, Elmar Eisemann, Jan C. van Gemert
Deep learning has improved vanishing point detection in images.
1 code implementation • 18 Feb 2022 • Alexander Vieth, Anna Vilanova, Boudewijn Lelieveldt, Elmar Eisemann, Thomas Höllt
In this paper, we present a method for incorporating spatial neighborhood information into distance-based dimensionality reduction methods, such as t-Distributed Stochastic Neighbor Embedding (t-SNE).
1 code implementation • 16 Nov 2021 • Ruben Wiersma, Ahmad Nasikun, Elmar Eisemann, Klaus Hildebrandt
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.
Ranked #6 on 3D Part Segmentation on ShapeNet-Part
1 code implementation • SIGGRAPH 2020 • Ruben Wiersma, Elmar Eisemann, Klaus Hildebrandt
We propose a network architecture for surfaces that consists of vector-valued, rotation-equivariant features.
no code implementations • 25 Jul 2019 • Tom Viering, Ziqi Wang, Marco Loog, Elmar Eisemann
This illustrates that GradCAM cannot explain the decision of every CNN and provides a proof of concept showing that it is possible to obfuscate the inner workings of a CNN.
2 code implementations • 28 May 2018 • Nicola Pezzotti, Julian Thijssen, Alexander Mordvintsev, Thomas Hollt, Baldur van Lew, Boudewijn P. F. Lelieveldt, Elmar Eisemann, Anna Vilanova
The t-distributed Stochastic Neighbor Embedding (tSNE) algorithm has become in recent years one of the most used and insightful techniques for the exploratory data analysis of high-dimensional data.
no code implementations • 5 Dec 2015 • Nicola Pezzotti, Boudewijn P. F. Lelieveldt, Laurens van der Maaten, Thomas Höllt, Elmar Eisemann, Anna Vilanova
Progressive Visual Analytics aims at improving the interactivity in existing analytics techniques by means of visualization as well as interaction with intermediate results.