204 citations · 609 across the 30 of their papers we have counts for
8 papers · 1 filter
Weakly-Supervised Optical Flow Estimation for Time-of-Flight
Michael Schelling, Pedro Hermosilla, Timo Ropinski
Indirect Time-of-Flight (iToF) cameras are a widespread type of 3D sensor, which perform multiple captures to obtain depth values of the captured scene. While recent approaches to…
Neural Activation Patterns (NAPs): Visual Explainability of Learned Concepts
Alex Bäuerle, Daniel Jönsson, Timo Ropinski
A key to deciphering the inner workings of neural networks is understanding what a model has learned. Promising methods for discovering learned features are based on analyzing acti…
Monocular Depth Decomposition of Semi-Transparent Volume Renderings
Dominik Engel, Sebastian Hartwig, Timo Ropinski
Neural networks have shown great success in extracting geometric information from color images. Especially, monocular depth estimation networks are increasingly reliable in real-wo…
Contrastive Representation Learning for 3D Protein Structures
Pedro Hermosilla, Timo Ropinski
Learning from 3D protein structures has gained wide interest in protein modeling and structural bioinformatics. Unfortunately, the number of available structures is orders of magni…
Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization
Ngan Nguyen, Feng Liang, Dominik Engel +4
We propose a new microscopy simulation system that can depict atomistic models in a micrograph visual style, similar to results of physical electron microscopy imaging. This system…
Clean Implicit 3D Structure from Noisy 2D STEM Images
Hannah Kniesel, Timo Ropinski, Tim Bergner +5
Scanning Transmission Electron Microscopes (STEMs) acquire 2D images of a 3D sample on the scale of individual cell components. Unfortunately, these 2D images can be too noisy to b…
Gaussian Mixture Convolution Networks
Adam Celarek, Pedro Hermosilla, Bernhard Kerbl +2
This paper proposes a novel method for deep learning based on the analytical convolution of multidimensional Gaussian mixtures. In contrast to tensors, these do not suffer from the…
Visual Identification of Problematic Bias in Large Label Spaces
Alex Bäuerle, Aybuke Gul Turker, Ken Burke +4
While the need for well-trained, fair ML systems is increasing ever more, measuring fairness for modern models and datasets is becoming increasingly difficult as they grow at an un…