16 citations · 26 across the 8 of their papers we have counts for
9 papers
Towards Learning a Generalizable 3D Scene Representation from 2D Observations
Martin Gromniak, Jan-Gerrit Habekost, Sebastian Kamp +2
We introduce a Generalizable Neural Radiance Field approach for predicting 3D workspace occupancy from egocentric robot observations. Unlike prior methods operating in camera-centr…
Neural Field Conditioning Strategies for 2D Semantic Segmentation
Martin Gromniak, Sven Magg, Stefan Wermter
Neural fields are neural networks which map coordinates to a desired signal. When a neural field should jointly model multiple signals, and not memorize only one, it needs to be co…
A Closer Look at Reward Decomposition for High-Level Robotic Explanations
Wenhao Lu, Xufeng Zhao, Sven Magg +3
Explaining the behaviour of intelligent agents learned by reinforcement learning (RL) to humans is challenging yet crucial due to their incomprehensible proprioceptive states, vari…
Robotic Tissue Sampling for Safe Post-mortem Biopsy in Infectious Corpses
Maximilian Neidhardt, Stefan Gerlach, Robin Mieling +12
In pathology and legal medicine, the histopathological and microbiological analysis of tissue samples from infected deceased is a valuable information for developing treatment stra…
Needle tip force estimation by deep learning from raw spectral OCT data
M. Gromniak, N. Gessert, T. Saathoff +1
Purpose. Needle placement is a challenging problem for applications such as biopsy or brachytherapy. Tip force sensing can provide valuable feedback for needle navigation inside th…
High-Speed Markerless Tissue Motion Tracking Using Volumetric Optical Coherence Tomography Images
Matthias Schlüter, Lukas Glandorf, Johanna Sprenger +4
Modern optical coherence tomography (OCT) devices provide volumetric images with micrometer-scale spatial resolution and a temporal resolution beyond video rate. In this work, we a…
Towards Deep Learning-Based EEG Electrode Detection Using Automatically Generated Labels
Nils Gessert, Martin Gromniak, Marcel Bengs +2
Electroencephalography (EEG) allows for source measurement of electrical brain activity. Particularly for inverse localization, the electrode positions on the scalp need to be know…
Two-path 3D CNNs for calibration of system parameters for OCT-based motion compensation
Nils Gessert, Martin Gromniak, Matthias Schlüter +1
Automatic motion compensation and adjustment of an intraoperative imaging modality's field of view is a common problem during interventions. Optical coherence tomography (OCT) is a…
Towards Head Motion Compensation Using Multi-Scale Convolutional Neural Networks
Omer Rajput, Nils Gessert, Martin Gromniak +2
Head pose estimation and tracking is useful in variety of medical applications. With the advent of RGBD cameras like Kinect, it has become feasible to do markerless tracking by est…