Structure preserving implicit shape encoding via flow regularization

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Abstract

Recently, implicit neural representations (INRs) emerged as an effective method for reconstructing shapes. Several of such methods transform templates to target shapes. Current template-based methods lack proper regularization. In this work, we add a novel regularization to a deformable template approach and discuss the benefits of this regularization with a simple test case.
Original languageEnglish
Number of pages4
Publication statusPublished - 18 Nov 2022
EventGeometric Deep Learning in Medical Image Analysis, GeoMedIA 2022 - Hotel CASA, Amsterdam, Netherlands
Duration: 18 Nov 202218 Nov 2022

Conference

ConferenceGeometric Deep Learning in Medical Image Analysis, GeoMedIA 2022
Abbreviated titleGeoMedIA 2022
Country/TerritoryNetherlands
CityAmsterdam
Period18/11/2218/11/22

Keywords

  • Latent space
  • Shape reconstruction
  • Implicit neural representation
  • Neural ODE

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