Search Results for author: Reinhard Klein

Found 17 papers, 6 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

FPO++: Efficient Encoding and Rendering of Dynamic Neural Radiance Fields by Analyzing and Enhancing Fourier PlenOctrees

no code implementations31 Oct 2023 Saskia Rabich, Patrick Stotko, Reinhard Klein

Fourier PlenOctrees have shown to be an efficient representation for real-time rendering of dynamic Neural Radiance Fields (NeRF).

TraM-NeRF: Tracing Mirror and Near-Perfect Specular Reflections through Neural Radiance Fields

1 code implementation16 Oct 2023 Leif Van Holland, Ruben Bliersbach, Jan U. Müller, Patrick Stotko, Reinhard Klein

Implicit representations like Neural Radiance Fields (NeRF) showed impressive results for photorealistic rendering of complex scenes with fine details.

Neural inverse procedural modeling of knitting yarns from images

no code implementations1 Mar 2023 Elena Trunz, Jonathan Klein, Jan Müller, Lukas Bode, Ralf Sarlette, Michael Weinmann, Reinhard Klein

We investigate the capabilities of neural inverse procedural modeling to infer high-quality procedural yarn models with fiber-level details from single images of depicted yarn samples.

Efficient 3D Reconstruction, Streaming and Visualization of Static and Dynamic Scene Parts for Multi-client Live-telepresence in Large-scale Environments

no code implementations25 Nov 2022 Leif Van Holland, Patrick Stotko, Stefan Krumpen, Reinhard Klein, Michael Weinmann

Despite the impressive progress of telepresence systems for room-scale scenes with static and dynamic scene entities, expanding their capabilities to scenarios with larger dynamic environments beyond a fixed size of a few square-meters remains challenging.

3D Reconstruction

BoundED: Neural Boundary and Edge Detection in 3D Point Clouds via Local Neighborhood Statistics

no code implementations24 Oct 2022 Lukas Bode, Michael Weinmann, Reinhard Klein

Extracting high-level structural information from 3D point clouds is challenging but essential for tasks like urban planning or autonomous driving requiring an advanced understanding of the scene at hand.

Autonomous Driving Edge Detection

Incomplete Gamma Kernels: Generalizing Locally Optimal Projection Operators

no code implementations2 May 2022 Patrick Stotko, Michael Weinmann, Reinhard Klein

We present incomplete gamma kernels, a generalization of Locally Optimal Projection (LOP) operators.

Surface 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.

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.

Bonn Activity Maps: Dataset Description

no code implementations13 Dec 2019 Julian Tanke, Oh-Hun Kwon, Patrick Stotko, Radu Alexandru Rosu, Michael Weinmann, Hassan Errami, Sven Behnke, Maren Bennewitz, Reinhard Klein, Andreas Weber, Angela Yao, Juergen Gall

The key prerequisite for accessing the huge potential of current machine learning techniques is the availability of large databases that capture the complex relations of interest.

Activity Recognition

Orthogonal Wasserstein GANs

no code implementations29 Nov 2019 Jan Müller, Reinhard Klein, Michael Weinmann

In current state-of-the-art Wasserstein-GANs this constraint is enforced via gradient norm regularization.

SLAMCast: Large-Scale, Real-Time 3D Reconstruction and Streaming for Immersive Multi-Client Live Telepresence

no code implementations9 May 2018 Patrick Stotko, Stefan Krumpen, Matthias B. Hullin, Michael Weinmann, Reinhard Klein

Real-time 3D scene reconstruction from RGB-D sensor data, as well as the exploration of such data in VR/AR settings, has seen tremendous progress in recent years.

Human-Computer Interaction

Efficient Unsupervised Temporal Segmentation of Motion Data

no code implementations22 Oct 2015 Björn Krüger, Anna Vögele, Tobias Willig, Angela Yao, Reinhard Klein, Andreas Weber

We introduce a method for automated temporal segmentation of human motion data into distinct actions and compositing motion primitives based on self-similar structures in the motion sequence.

Clustering Markerless Motion Capture +1

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