no code implementations • 28 Jul 2016 • Gernot Riegler, David Ferstl, Matthias Rüther, Horst Bischof
In this paper we present a novel method to increase the spatial resolution of depth images.
no code implementations • 27 Jul 2016 • Gernot Riegler, Matthias Rüther, Horst Bischof
We demonstrate that it is feasible to train our method solely on synthetic data that we generate in large quantities for this task.
no code implementations • 26 Apr 2013 • Gernot Riegler, Thomas Pock, Werner Pötzi, Astrid Veronig
The information produced by our method can be used for near real-time alerts and the statistical analysis of existing data by solar physicists.
no code implementations • ICCV 2015 • Gernot Riegler, Samuel Schulter, Matthias Ruther, Horst Bischof
However, this setting is not realistic for practical applications, because the blur is typically different for each test image.
no code implementations • CVPR 2019 • Gernot Riegler, Yiyi Liao, Simon Donne, Vladlen Koltun, Andreas Geiger
We propose a technique for depth estimation with a monocular structured-light camera, i. e., a calibrated stereo set-up with one camera and one laser projector.
no code implementations • 17 Mar 2023 • Tianhao Wu, Hanxue Liang, Fangcheng Zhong, Gernot Riegler, Shimon Vainer, Cengiz Oztireli
While neural radiance field (NeRF) based methods can model semi-transparency and achieve photo-realistic quality in synthesized novel views, their volumetric geometry representation tightly couples geometry and opacity, and therefore cannot be easily converted into surfaces without introducing artifacts.
1 code implementation • CVPR 2016 • Markus Oberweger, Gernot Riegler, Paul Wohlhart, Vincent Lepetit
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 so far.
1 code implementation • 4 Apr 2017 • Gernot Riegler, Ali Osman Ulusoy, Horst Bischof, Andreas Geiger
In this paper, we present a learning based approach to depth fusion, i. e., dense 3D reconstruction from multiple depth images.
3 code implementations • CVPR 2021 • Gernot Riegler, Vladlen Koltun
The core of SVS is view-dependent on-surface feature aggregation, in which directional feature vectors at each 3D point are processed to produce a new feature vector for a ray that maps this point into the new target view.
1 code implementation • ECCV 2020 • Gernot Riegler, Vladlen Koltun
We present a method for novel view synthesis from input images that are freely distributed around a scene.
1 code implementation • CVPR 2017 • Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
We present OctNet, a representation for deep learning with sparse 3D data.
5 code implementations • 15 Oct 2020 • Kai Zhang, Gernot Riegler, Noah Snavely, Vladlen Koltun
Neural Radiance Fields (NeRF) achieve impressive view synthesis results for a variety of capture settings, including 360 capture of bounded scenes and forward-facing capture of bounded and unbounded scenes.