Search Results for author: Nils Wandel

Found 8 papers, 4 papers with code

Physics-guided Shape-from-Template: Monocular Video Perception through Neural Surrogate Models

1 code implementation21 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.

3D Reconstruction

Spline-PINN: Approaching PDEs without Data using Fast, Physics-Informed Hermite-Spline CNNs

3 code implementations15 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.

Physics-Informed Deep Learning of Incompressible Fluid Dynamics

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.

Teaching the Incompressible Navier-Stokes Equations to Fast Neural Surrogate Models in 3D

3 code implementations22 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.

Robust Skeletonization for Plant Root Structure Reconstruction from MRI

no code implementations27 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.

Learning Incompressible Fluid Dynamics from Scratch -- Towards Fast, Differentiable Fluid Models that Generalize

3 code implementations15 Jun 2020 Nils Wandel, Michael Weinmann, Reinhard Klein

Our models significantly outperform a recent differentiable fluid solver in terms of computational speed and accuracy.

3D U-Net for Segmentation of Plant Root MRI Images in Super-Resolution

no code implementations21 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.

Super-Resolution

Complex Valued Gated Auto-encoder for Video Frame Prediction

no code implementations8 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.

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