Hi, I’m Dominik! I’m a PhD student at Ulm University, where I am part of the Visual Computing Group, supervised by Prof. Dr. Timo Ropinski. My main interest is deep learning on visual data. Currently I’m specifically interested in applying deep learning to volume rendering.
Dominik Engel et al.: "Monocular Depth Estimation for Semi-Transparent Volume Renderings" arXiv.
Predicting relevant depth structures in semi-transparent renderings using neural nets
Nguyen et al.: "Differentiable Electron Microscopy Simulation: Methods and Applications for Visualization" arXiv:2205.04464
Differentiable transmission electron microscopy simulator
Ngan Nguyen et al.: "Finding Nano-Ötzi: Semi-Supervised Volume Visualization for Cryo-Electron Tomography"in IEEE Transactions on Visualization and Computer Graphics (2022).
Automatic visualization of Cryo-ET data using deep learning based 3D segmentation
Michael Stegmaier et al.: "Property-Based Testing for Visualization Development" in The Eurographics Association VisGap Workshop (2021).
Property-based Testing for Visualization Development, based on Inviwo
Dominik Engel and Timo Ropinski: "Deep Volumetric Ambient Occlusion" in IEEE Transactions on Visualization and Computer Graphics (2020).
CNN-based volumetric lighting and guidelines for volumetric illumination learning
Open Source Projects
Taichi-based differentiable Volume renderer for use with PyTorch
PyTorch-based framework for efficient volume data loading, caching, transformations and more
Modular C++ Framework for rapid visualization prototyping