31 citations · 82 across the 17 of their papers we have counts for
20 papers
Decoupled Boundary Handling in SPH
Rustam Akhunov, Andreas Kolb
Particle-based boundary representations are frequently used in Smoothed Particle Hydrodynamics (SPH) due to their simple integration into fluid solvers. Commonly, incompressible fl…
Differentiable Sensor Layouts for End-to-End Learning of Task-Specific Camera Parameters
Hendrik Sommerhoff, Shashank Agnihotri, Mohamed Saleh +3
The success of deep learning is frequently described as the ability to train all parameters of a network on a specific application in an end-to-end fashion. Yet, several design cho…
Neural Volume Super-Resolution
Yuval Bahat, Yuxuan Zhang, Hendrik Sommerhoff +2
Neural volumetric representations have become a widely adopted model for radiance fields in 3D scenes. These representations are fully implicit or hybrid function approximators of…
Glyph from Icon -- Automated Generation of Metaphoric Glyphs
Dmitri Presnov, Andreas Kolb
Metaphoric glyphs enhance the readability and learnability of abstract glyphs used for the visualization of quantitative multidimensional data by building upon graphical entities t…
Light Field Implicit Representation for Flexible Resolution Reconstruction
Paramanand Chandramouli, Hendrik Sommerhoff, Andreas Kolb
Inspired by the recent advances in implicitly representing signals with trained neural networks, we aim to learn a continuous representation for narrow-baseline 4D light fields. We…
A Generative Model for Generic Light Field Reconstruction
Paramanand Chandramouli, Kanchana Vaishnavi Gandikota, Andreas Goerlitz +2
Recently deep generative models have achieved impressive progress in modeling the distribution of training data. In this work, we present for the first time a generative model for…