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Michael Möller

6 papers hereh-index 211.7k citations48 works total

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

author position
  • first author1
  • middle author2
  • last author3

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CV4
  • cs.LG1
  • math.OC1
same name
  • Michael Möller — 2 papers
  • Michael Möller — 1 paper
  • Michael Möller — 1 paper, h 0

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
20152020
most citedPoint-wise Map Recovery and Refinement from Functional Correspondence

54 citations · 54 across the 2 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2020

Learning to Identify Physical Parameters from Video Using Differentiable Physics

Rama Krishna Kandukuri, Jan Achterhold, Michael Möller +1

Video representation learning has recently attracted attention in computer vision due to its applications for activity and scene forecasting or vision-based planning and control. V…

cs.CV2019

Training Auto-encoder-based Optimizers for Terahertz Image Reconstruction

Tak Ming Wong, Matthias Kahl, Peter Haring Bolívar +2

Terahertz (THz) sensing is a promising imaging technology for a wide variety of different applications. Extracting the interpretable and physically meaningful parameters for such a…

cs.CV2018

Lifting Layers: Analysis and Applications

Peter Ochs, Tim Meinhardt, Laura Leal-Taixe +1

The great advances of learning-based approaches in image processing and computer vision are largely based on deeply nested networks that compose linear transfer functions with suit…

cs.CV2015★ 54 cited

Point-wise Map Recovery and Refinement from Functional Correspondence

Emanuele Rodolà, Michael Moeller, Daniel Cremers

Since their introduction in the shape analysis community, functional maps have met with considerable success due to their ability to compactly represent dense correspondences betwe…

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