Timo Ropinski

Timo Ropinski

Universität Ulm

H-index: 34

Europe-Germany

About Timo Ropinski

Timo Ropinski, With an exceptional h-index of 34 and a recent h-index of 23 (since 2020), a distinguished researcher at Universität Ulm, specializes in the field of Visual Computing, Data Visualization, Computer Graphics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction

Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics

Sgrec3d: Self-supervised 3d scene graph learning via object-level scene reconstruction

Attention-Guided Masked Autoencoders For Learning Image Representations

Spatially Guiding Unsupervised Semantic Segmentation Through Depth-Informed Feature Distillation and Sampling

Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging: Evaluation of Contrastive and Masked Autoencoder Methods

Differentiable electron microscopy simulation: methods and applications for visualization

Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships

Timo Ropinski Information

University

Position

___

Citations(all)

4278

Citations(since 2020)

1992

Cited By

2982

hIndex(all)

34

hIndex(since 2020)

23

i10Index(all)

92

i10Index(since 2020)

43

Email

University Profile Page

Universität Ulm

Google Scholar

View Google Scholar Profile

Timo Ropinski Skills & Research Interests

Visual Computing

Data Visualization

Computer Graphics

Top articles of Timo Ropinski

Title

Journal

Author(s)

Publication Date

Lang3DSG: Language-based contrastive pre-training for 3D Scene Graph prediction

arXiv preprint arXiv:2310.16494

Sebastian Koch

Pedro Hermosilla

Narunas Vaskevicius

Mirco Colosi

Timo Ropinski

2023/10/25

Evaluating Text to Image Synthesis: Survey and Taxonomy of Image Quality Metrics

Sebastian Hartwig

Dominik Engel

Leon Sick

Hannah Kniesel

Tristan Payer

...

2024/3/18

Sgrec3d: Self-supervised 3d scene graph learning via object-level scene reconstruction

Sebastian Koch

Pedro Hermosilla

Narunas Vaskevicius

Mirco Colosi

Timo Ropinski

2024

Attention-Guided Masked Autoencoders For Learning Image Representations

arXiv preprint arXiv:2402.15172

Leon Sick

Dominik Engel

Pedro Hermosilla

Timo Ropinski

2024/2/23

Spatially Guiding Unsupervised Semantic Segmentation Through Depth-Informed Feature Distillation and Sampling

arXiv preprint arXiv:2309.12378

Leon Sick

Dominik Engel

Pedro Hermosilla

Timo Ropinski

2023/9/21

Self-supervised Pre-training for Dealing with Small Datasets in Deep Learning for Medical Imaging: Evaluation of Contrastive and Masked Autoencoder Methods

Daniel Wolf

Tristan Payer

Catharina S Lisson

Christoph G Lisson

Meinrad Beer

...

2024/2/20

Differentiable electron microscopy simulation: methods and applications for visualization

arXiv preprint arXiv:2205.04464

Ngan Nguyen

Feng Liang

Dominik Engel

Ciril Bohak

Peter Wonka

...

2022/5/8

Open3DSG: Open-Vocabulary 3D Scene Graphs from Point Clouds with Queryable Objects and Open-Set Relationships

arXiv preprint arXiv:2402.12259

Sebastian Koch

Narunas Vaskevicius

Mirco Colosi

Pedro Hermosilla

Timo Ropinski

2024/2/19

Weakly Supervised Virus Capsid Detection with Image-Level Annotations in Electron Microscopy Images

Hannah Kniesel

Leon Sick

Tristan Payer

Tim Bergner

Kavitha Shaga Devan

...

2023/10/13

Clusternet: A perception-based clustering model for scattered data

arXiv preprint arXiv:2304.14185

Sebastian Hartwig

Christian van Onzenoodt

Dominik Engel

Pedro Hermosilla

Timo Ropinski

2023/4/27

Sedierungstraining für die gastrointestinale Endoskopie: Virtual Reality vs konventionelles Seminar–eine randomisierte Studie

Zeitschrift für Gastroenterologie

T Malzacher

D Henninger

M Engelke

J Kreiser

T Ropinski

...

2023/8

Weakly-Supervised Optical Flow Estimation for Time-of-Flight

Michael Schelling

Pedro Hermosilla

Timo Ropinski

2023

A3GC-IP: Attention-oriented adjacency adaptive recurrent graph convolutions for human pose estimation from sparse inertial measurements

Computers & Graphics

Patrik Puchert

Timo Ropinski

2023/12/1

LLMMaps – A Visual Metaphor for Stratified Evaluation of Large Language Models

arXiv preprint arXiv:2304.00457

Patrik Puchert

Poonam Poonam

Christian van Onzenoodt

Timo Ropinski

2023/4/2

Medical volume segmentation by overfitting sparsely annotated data

Journal of Medical Imaging

Tristan Payer

Faraz Nizamani

Meinrad Beer

Michael Götz

Timo Ropinski

2023/7/1

Self-supervised pre-training with contrastive and masked autoencoder methods for dealing with small datasets in deep learning for medical imaging

Scientific Reports

Daniel Wolf

Tristan Payer

Catharina Silvia Lisson

Christoph Gerhard Lisson

Meinrad Beer

...

2023/11/20

Levels of explicability for medical artificial intelligence: What do we normatively need and what can we technically reach?

Ethik in der Medizin

Frank Ursin

Felix Lindner

Timo Ropinski

Sabine Salloch

Cristian Timmermann

2023/6

Chair Message

IEEE Pacific Visualization Symposium

Chuck Hansen

Ivan Viola

Xiaoru Yuan

Jinwook Seo

Yu Shuen Wang

2016/5/4

Leveraging Self-Supervised Vision Transformers for Neural Transfer Function Design

arXiv preprint arXiv:2309.01408

Dominik Engel

Leon Sick

Timo Ropinski

2023/9/4

Semantic Hierarchical Exploration of Large Image Datasets

Alex Bäuerle

Christian van Onzenoodt

Daniel Jönsson

Timo Ropinski

2023

See List of Professors in Timo Ropinski University(Universität Ulm)

Co-Authors

H-index: 52
Frank Steinicke

Frank Steinicke

Universität Hamburg

H-index: 51
Bernhard Preim

Bernhard Preim

Otto-von-Guericke-Universität Magdeburg

H-index: 46
Gerd Bruder

Gerd Bruder

University of Central Florida

H-index: 43
Anders Ynnerman

Anders Ynnerman

Linköpings Universitet

H-index: 42
Tobias Ritschel

Tobias Ritschel

University College London

H-index: 35
Markus Hadwiger

Markus Hadwiger

King Abdullah University of Science and Technology

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