1 code implementation • 21 Nov 2023 • David Stotko, Nils Wandel, Reinhard Klein
3D reconstruction of dynamic scenes is a long-standing problem in computer graphics and increasingly difficult the less information is available.
3 code implementations • 15 Sep 2021 • Nils Wandel, Michael Weinmann, Michael Neidlin, Reinhard Klein
Second, convolutional neural networks provide fast inference and generalize but either require large amounts of training data or a physics-constrained loss based on finite differences that can lead to inaccuracies and discretization artifacts.
no code implementations • ICLR 2021 • Nils Wandel, Michael Weinmann, Reinhard Klein
Moreover, the trained neural networks offer a differentiable update step to advance the fluid simulation in time and, thus, can be used as efficient differentiable fluid solvers.
3 code implementations • 22 Dec 2020 • Nils Wandel, Michael Weinmann, Reinhard Klein
Our method indicates strong improvements in terms of accuracy, speed and generalization capabilities over current 3D NN-based fluid models.
no code implementations • 27 Oct 2020 • Jannis Horn, Yi Zhao, Nils Wandel, Magdalena Landl, Andrea Schnepf, Sven Behnke
Structural reconstruction of plant roots from MRI is challenging, because of low resolution and low signal-to-noise ratio of the 3D measurements which may lead to disconnectivities and wrongly connected roots.
3 code implementations • 15 Jun 2020 • Nils Wandel, Michael Weinmann, Reinhard Klein
Our models significantly outperform a recent differentiable fluid solver in terms of computational speed and accuracy.
no code implementations • 21 Feb 2020 • Yi Zhao, Nils Wandel, Magdalena Landl, Andrea Schnepf, Sven Behnke
Magnetic resonance imaging (MRI) enables plant scientists to non-invasively study root system development and root-soil interaction.
no code implementations • 8 Mar 2019 • Niloofar Azizi, Nils Wandel, Sven Behnke
Then, we present how a complex neural network can learn such transformations and compare its performance and parameter efficiency to a real-valued gated autoencoder.