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U. Neumann

27 papers hereh-index 6515.2k citations299 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author3
  • last author24

Across the 27 of 27 papers where every author was matched, so the position is known.

fields
  • cs.CV25
  • cs.GR1
  • eess.IV1
same name
  • U. Neumann — 12 papers, h 16
  • U. Neumann — 1 paper, h 1
  • U. Neumann — 1 paper, h 7
  • U. Neumann — 1 paper, h 6

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20162023
most citedLearning to Prune Filters in Convolutional Neural Networks

27 citations · 80 across the 15 of their papers we have counts for

collaborators
Showing 2019 · cs.CVShow all

4 papers · 2 filters

cs.CV2019

Grid-GCN for Fast and Scalable Point Cloud Learning

Qiangeng Xu, Xudong Sun, Cho-Ying Wu +2

Due to the sparsity and irregularity of the point cloud data, methods that directly consume points have become popular. Among all point-based models, graph convolutional networks (…

cs.CV2019

Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion

Yiqi Zhong, Cho-Ying Wu, Suya You +1

In this paper, we propose our Correlation For Completion Network (CFCNet), an end-to-end deep learning model that uses the correlation between two data sources to perform sparse de…

cs.CV2019

DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction

Qiangeng Xu, Weiyue Wang, Duygu Ceylan +2

Reconstructing 3D shapes from single-view images has been a long-standing research problem. In this paper, we present DISN, a Deep Implicit Surface Network which can generate a hig…

cs.CV2019★ 10 cited

3DN: 3D Deformation Network

Weiyue Wang, Duygu Ceylan, Radomir Mech +1

Applications in virtual and augmented reality create a demand for rapid creation and easy access to large sets of 3D models. An effective way to address this demand is to edit or d…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.