Search Results for author: Timo Ropinski

Found 14 papers, 10 papers with code

Human Pose Estimation from Sparse Inertial Measurements through Recurrent Graph Convolution

no code implementations23 Jul 2021 Patrik Puchert, Timo Ropinski

We propose the adjacency adaptive graph convolutional long-short term memory network (AAGC-LSTM) for human pose estimation from sparse inertial measurements, obtained from only 6 measurement units.

Data Augmentation Pose Estimation

Data-driven deep density estimation

1 code implementation23 Jul 2021 Patrik Puchert, Pedro Hermosilla, Tobias Ritschel, Timo Ropinski

Density estimation plays a crucial role in many data analysis tasks, as it infers a continuous probability density function (PDF) from discrete samples.

Density Estimation

Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography

no code implementations4 Apr 2021 Ngan Nguyen, Ciril Bohak, Dominik Engel, Peter Mindek, Ondřej Strnad, Peter Wonka, Sai Li, Timo Ropinski, Ivan Viola

Our technique shows the high impact in target sciences for visual data analysis of very noisy volumes that cannot be visualized with existing techniques.

Electron Tomography

exploRNN: Understanding Recurrent Neural Networks through Visual Exploration

no code implementations9 Dec 2020 Alex Bäuerle, Raphael Störk, Timo Ropinski

Due to the success of deep learning and its growing job market, students and researchers from many areas are getting interested in learning about deep learning technologies.

Deep Volumetric Ambient Occlusion

1 code implementation19 Aug 2020 Dominik Engel, Timo Ropinski

We present a novel deep learning based technique for volumetric ambient occlusion in the context of direct volume rendering.

Enabling Viewpoint Learning through Dynamic Label Generation

1 code implementation10 Mar 2020 Michael Schelling, Pedro Hermosilla, Pere-Pau Vazquez, Timo Ropinski

Optimal viewpoint prediction is an essential task in many computer graphics applications.

Classifying the classifier: dissecting the weight space of neural networks

1 code implementation13 Feb 2020 Gabriel Eilertsen, Daniel Jönsson, Timo Ropinski, Jonas Unger, Anders Ynnerman

of neural network classifiers, and train a large number of models to represent the weight space.

Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning

1 code implementation ICCV 2019 Pedro Hermosilla, Tobias Ritschel, Timo Ropinski

We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only.

Denoising

Training Object Detectors on Synthetic Images Containing Reflecting Materials

no code implementations29 Mar 2019 Sebastian Hartwig, Timo Ropinski

While synthesized data sets can be used to overcome this challenge, it is important that these data sets close the reality gap, i. e., a model trained on synthetic image data is able to generalize to real images.

Image Generation

Net2Vis -- A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations

1 code implementation11 Feb 2019 Alex Bäuerle, Christian van Onzenoodt, Timo Ropinski

To convey neural network architectures in publications, appropriate visualizations are of great importance.

Deep-learning the Latent Space of Light Transport

1 code implementation12 Nov 2018 Pedro Hermosilla, Sebastian Maisch, Tobias Ritschel, Timo Ropinski

Thus, we suggest a two-stage operator comprising of a 3D network that first transforms the point cloud into a latent representation, which is later on projected to the 2D output image using a dedicated 3D-2D network in a second step.

Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks

1 code implementation9 Aug 2018 Alex Bäuerle, Heiko Neumann, Timo Ropinski

We thus propose a novel approach that uses the power of pretrained classifiers to visually guide users to noisy labels, and let them interactively check error candidates, to iteratively improve the training data set.

Classification General Classification +1

Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds

1 code implementation5 Jun 2018 Pedro Hermosilla, Tobias Ritschel, Pere-Pau Vázquez, Àlvar Vinacua, Timo Ropinski

We propose an efficient and effective method to learn convolutions for non-uniformly sampled point clouds, as they are obtained with modern acquisition techniques.

Point Cloud Segmentation

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