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A convex variational model for learning convolutional image atoms from incomplete data
Antonin Chambolle, Martin Holler Thomas Pock
A variational model for learning convolutional image atoms from corrupted and/or incomplete data is introduced and analyzed both in function space and numerically. Building on lift…
Learning Energy Based Inpainting for Optical Flow
Christoph Vogel, Patrick Knöbelreiter, Thomas Pock
Modern optical flow methods are often composed of a cascade of many independent steps or formulated as a black box neural network that is hard to interpret and analyze. In this wor…
Variational 3D-PIV with Sparse Descriptors
Katrin Lasinger, Christoph Vogel, Thomas Pock +1
3D Particle Imaging Velocimetry (3D-PIV) aim to recover the flow field in a volume of fluid, which has been seeded with tracer particles and observed from multiple camera viewpoint…
3D Fluid Flow Estimation with Integrated Particle Reconstruction
Katrin Lasinger, Christoph Vogel, Thomas Pock +1
The standard approach to densely reconstruct the motion in a volume of fluid is to inject high-contrast tracer particles and record their motion with multiple high-speed cameras. A…
Robust Deformation Estimation in Wood-Composite Materials using Variational Optical Flow
Markus Hofinger, Thomas Pock, Thomas Moosbrugger
Wood-composite materials are widely used today as they homogenize humidity related directional deformations. Quantification of these deformations as coefficients is important for c…