7 citations · 12 across the 5 of their papers we have counts for
6 papers
Frame-To-Frame Consistent Semantic Segmentation
Manuel Rebol, Patrick Knöbelreiter
In this work, we aim for temporally consistent semantic segmentation throughout frames in a video. Many semantic segmentation algorithms process images individually which leads to…
Belief Propagation Reloaded: Learning BP-Layers for Labeling Problems
Patrick Knöbelreiter, Christian Sormann, Alexander Shekhovtsov +2
It has been proposed by many researchers that combining deep neural networks with graphical models can create more efficient and better regularized composite models. The main diffi…
Learned Collaborative Stereo Refinement
Patrick Knöbelreiter, Thomas Pock
In this work, we propose a learning-based method to denoise and refine disparity maps of a given stereo method. The proposed variational network arises naturally from unrolling the…
Self-Supervised Learning for Stereo Reconstruction on Aerial Images
Patrick Knöbelreiter, Christoph Vogel, Thomas Pock
Recent developments established deep learning as an inevitable tool to boost the performance of dense matching and stereo estimation. On the downside, learning these networks requi…
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…
End-to-End Training of Hybrid CNN-CRF Models for Stereo
Patrick Knöbelreiter, Christian Reinbacher, Alexander Shekhovtsov +1
We propose a novel and principled hybrid CNN+CRF model for stereo estimation. Our model allows to exploit the advantages of both, convolutional neural networks (CNNs) and condition…