Search Results for author: Gernot Riegler

Found 11 papers, 6 papers with code

Stable View Synthesis

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.

NeRF++: Analyzing and Improving Neural Radiance Fields

5 code implementations15 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.

Free View Synthesis

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.

Novel View Synthesis

Connecting the Dots: Learning Representations for Active Monocular Depth Estimation

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.

Depth Prediction Monocular Depth Estimation

OctNetFusion: Learning Depth Fusion from Data

1 code implementation4 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.

3D Reconstruction

A Deep Primal-Dual Network for Guided Depth Super-Resolution

no code implementations28 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.


ATGV-Net: Accurate Depth Super-Resolution

no code implementations27 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.

Depth Map Super-Resolution Image Super-Resolution +1

Efficiently Creating 3D Training Data for Fine Hand Pose Estimation

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.

Hand Pose Estimation

Filament and Flare Detection in Hα image sequences

no code implementations26 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.

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