510 citations · 652 across the 8 of their papers we have counts for
4 papers · 1 filter
OctNet: Learning Deep 3D Representations at High Resolutions
Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
We present OctNet, a representation for deep learning with sparse 3D data. In contrast to existing models, our representation enables 3D convolutional networks which are both deep…
A Deep Primal-Dual Network for Guided Depth Super-Resolution
Gernot Riegler, David Ferstl, Matthias Rüther +1
In this paper we present a novel method to increase the spatial resolution of depth images. We combine a deep fully convolutional network with a non-local variational method in a d…
ATGV-Net: Accurate Depth Super-Resolution
Gernot Riegler, Matthias Rüther, Horst Bischof
In this work we present a novel approach for single depth map super-resolution. Modern consumer depth sensors, especially Time-of-Flight sensors, produce dense depth measurements,…
Efficiently Creating 3D Training Data for Fine Hand Pose Estimation
Markus Oberweger, Gernot Riegler, Paul Wohlhart +1
While many recent hand pose estimation methods critically rely on a training set of labelled frames, the creation of such a dataset is a challenging task that has been overlooked s…