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researcher

A. Wieser

9 papers hereh-index 458.3k citations304 works total

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

author position
  • middle author3
  • last author6

Across the 9 of 9 papers where every author was matched, so the position is known.

fields
  • cs.CV3
  • stat.ML3
  • stat.AP2
  • cs.LG1
same name
  • A. Wieser — 5 papers, h 3
  • A. Wieser — 3 papers, h 8
  • A. Wieser — 2 papers, h 1
  • A. Wieser — 1 paper, h 2

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
20172021
most citedJaccard analysis and LASSO-based feature selection for location fingerprinting with limited computational complexity

1 citations · 3 across the 5 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2023★ 1 cited

DEFLOW: Self-supervised 3D Motion Estimation of Debris Flow

Liyuan Zhu, Yuru Jia, Shengyu Huang +4

Existing work on scene flow estimation focuses on autonomous driving and mobile robotics, while automated solutions are lacking for motion in nature, such as that exhibited by debr…

cs.CV2021

Weakly Supervised Learning of Rigid 3D Scene Flow

Zan Gojcic, Or Litany, Andreas Wieser +2

We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. At the co…

cs.CV2020

PREDATOR: Registration of 3D Point Clouds with Low Overlap

Shengyu Huang, Zan Gojcic, Mikhail Usvyatsov +2

We introduce PREDATOR, a model for pairwise point-cloud registration with deep attention to the overlap region. Different from previous work, our model is specifically designed to…

cs.CV2018

The Perfect Match: 3D Point Cloud Matching with Smoothed Densities

Zan Gojcic, Caifa Zhou, Jan D. Wegner +1

We propose 3DSmoothNet, a full workflow to match 3D point clouds with a siamese deep learning architecture and fully convolutional layers using a voxelized smoothed density value (…

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