Search Results for author: Stefan Gumhold

Found 12 papers, 8 papers with code

InFlow: Robust outlier detection utilizing Normalizing Flows

1 code implementation10 Jun 2021 Nishant Kumar, Pia Hanfeld, Michael Hecht, Michael Bussmann, Stefan Gumhold, Nico Hoffmannn

Normalizing flows are prominent deep generative models that provide tractable probability distributions and efficient density estimation.

Density Estimation Outlier Detection

FuseVis: Interpreting neural networks for image fusion using per-pixel saliency visualization

1 code implementation6 Dec 2020 Nishant Kumar, Stefan Gumhold

However, it is challenging to analyze the reliability of these CNNs for the image fusion tasks since no groundtruth is available.

Autonomous Driving

Visualisation of Medical Image Fusion and Translation for Accurate Diagnosis of High Grade Gliomas

1 code implementation26 Jan 2020 Nishant Kumar, Nico Hoffmann, Matthias Kirsch, Stefan Gumhold

The medical image fusion combines two or more modalities into a single view while medical image translation synthesizes new images and assists in data augmentation.

Data Augmentation Translation

Learning to Think Outside the Box: Wide-Baseline Light Field Depth Estimation with EPI-Shift

no code implementations19 Sep 2019 Titus Leistner, Hendrik Schilling, Radek Mackowiak, Stefan Gumhold, Carsten Rother

In order to work with wide-baseline light fields, we introduce the idea of EPI-Shift: To virtually shift the light field stack which enables to retain a small receptive field, independent of the disparity range.

Depth Estimation

Structural Similarity based Anatomical and Functional Brain Imaging Fusion

1 code implementation11 Aug 2019 Nishant Kumar, Nico Hoffmann, Martin Oelschlägel, Edmund Koch, Matthias Kirsch, Stefan Gumhold

Multimodal medical image fusion helps in combining contrasting features from two or more input imaging modalities to represent fused information in a single image.

SSIM

scenery -- Flexible Virtual Reality Visualisation on the Java VM

2 code implementations16 Jun 2019 Ulrik Günther, Tobias Pietzsch, Aryaman Gupta, Kyle I. S. Harrington, Pavel Tomancak, Stefan Gumhold, Ivo F. Sbalzarini

Life science today involves computational analysis of a large amount and variety of data, such as volumetric data acquired by state-of-the-art microscopes, or mesh data resulting from analysis of such data or simulations.

Graphics

Global Hypothesis Generation for 6D Object Pose Estimation

no code implementations CVPR 2017 Frank Michel, Alexander Kirillov, Eric Brachmann, Alexander Krull, Stefan Gumhold, Bogdan Savchynskyy, Carsten Rother

Most modern approaches solve this task in three steps: i) Compute local features; ii) Generate a pool of pose-hypotheses; iii) Select and refine a pose from the pool.

6D Pose Estimation using RGB

In situ, steerable, hardware-independent and data-structure agnostic visualization with ISAAC

1 code implementation28 Nov 2016 Alexander Matthes, Axel Huebl, René Widera, Sebastian Grottel, Stefan Gumhold, Michael Bussmann

The computation power of supercomputers grows faster than the bandwidth of their storage and network.

Distributed, Parallel, and Cluster Computing

DSAC - Differentiable RANSAC for Camera Localization

4 code implementations CVPR 2017 Eric Brachmann, Alexander Krull, Sebastian Nowozin, Jamie Shotton, Frank Michel, Stefan Gumhold, Carsten Rother

The most promising approach is inspired by reinforcement learning, namely to replace the deterministic hypothesis selection by a probabilistic selection for which we can derive the expected loss w. r. t.

Camera Localization Visual Localization

Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image

no code implementations CVPR 2016 Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother

In recent years, the task of estimating the 6D pose of object instances and complete scenes, i. e. camera localization, from a single input image has received considerable attention.

6D Pose Estimation 6D Pose Estimation using RGB +1

Learning Analysis-by-Synthesis for 6D Pose Estimation in RGB-D Images

no code implementations ICCV 2015 Alexander Krull, Eric Brachmann, Frank Michel, Michael Ying Yang, Stefan Gumhold, Carsten Rother

This is done by describing the posterior density of a particular object pose with a convolutional neural network (CNN) that compares an observed and rendered image.

6D Pose Estimation 6D Pose Estimation using RGB

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