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.
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.
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.
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.
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.
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.
1 code implementation • CVPR 2017 • Gernot Riegler, Ali Osman Ulusoy, Andreas Geiger
We present OctNet, a representation for deep learning with sparse 3D data.
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.
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.
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 • 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.