activity
20132023
most citedNeRF++: Analyzing and Improving Neural Radiance Fields

510 citations · 652 across the 8 of their papers we have counts for

collaborators

11 papers

cs.CV2023

Surf: Implicit Surface Reconstruction for Semi-Transparent and Thin Objects with Decoupled Geometry and Opacity

Tianhao Wu, Hanxue Liang, Fangcheng Zhong +4

Implicit surface representations such as the signed distance function (SDF) have emerged as a promising approach for image-based surface reconstruction. However, existing optimizat…

cs.CV2020

Stable View Synthesis

Gernot Riegler, Vladlen Koltun

We present Stable View Synthesis (SVS). Given a set of source images depicting a scene from freely distributed viewpoints, SVS synthesizes new views of the scene. The method operat…

cs.CV2020★ 510 cited

NeRF++: Analyzing and Improving Neural Radiance Fields

Kai Zhang, Gernot Riegler, Noah Snavely +1

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 bou…

cs.CV2020

Free View Synthesis

Gernot Riegler, Vladlen Koltun

We present a method for novel view synthesis from input images that are freely distributed around a scene. Our method does not rely on a regular arrangement of input views, can syn…

cs.CV2017★ 26 cited

OctNetFusion: Learning Depth Fusion from Data

Gernot Riegler, Ali Osman Ulusoy, Horst Bischof +1

In this paper, we present a learning based approach to depth fusion, i.e., dense 3D reconstruction from multiple depth images. The most common approach to depth fusion is based on…

cs.CV2016★ 34 cited

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…

cs.CV2016★ 11 cited

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…

cs.CV2016★ 25 cited

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,…

cs.CV2016

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…

astro-ph.SR2014★ 46 cited

Real-time flare detection in ground-based H imaging at Kanzelhöhe Observatory

Werner Pötzi, Astrid M. Veronig, Gernot Riegler +5

Kanzelhöhe Observatory (KSO) regularly performs high-cadence full-disk imaging of the solar chromosphere in the H and CaIIK spectrallines as well as the solar photosphere in whi…

cs.CV2013

Filament and Flare Detection in Hα image sequences

Gernot Riegler, Thomas Pock, Werner Pötzi +1

Solar storms can have a major impact on the infrastructure of the earth. Some of the causing events are observable from ground in the Hα spectral line. In this paper we propose a n…