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Radish: Radar Implicit Shapes Via Extreme-Wideband Sub-Terahertz Synthetic Aperture Radar

Sean M. Farrell, Kris Li, Edwin Vargas, Ashutosh Sabharwal, Ashok Veeraraghavan, Henry O. Everitt, Ariel Habshush, Li Zhang, Brian Woods

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We explore synthetic aperture radar (SAR) 3D imaging capabilities in the sub-THz band using a novel 110-260 GHz experimental testbed. We propose RADar Implicit SHapes (RADISH), a post-processing method that leverages the SAR's millimeter-level imaging resolution to estimate an object's 3D shape. RADISH first c onverts high-resolution SAR images to a detailed point cloud of a scanned object. The point cloud is then used to fit an implicit neural representation to the object's surface by approximating the signed distance function of the scene. We experimentally validate RADISH's ability to represent the salient geometric features of real-world 3D objects.

Original languageEnglish (US)
Title of host publication2025 IEEE Conference on Computational Imaging Using Synthetic Apertures, CISA 2025
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9798331515102
DOIs
StatePublished - 2025
Event2025 IEEE Conference on Computational Imaging Using Synthetic Apertures, CISA 2025 - College Park, United States
Duration: Jun 2 2025Jun 6 2025

Publication series

Name2025 IEEE Conference on Computational Imaging Using Synthetic Apertures, CISA 2025

Conference

Conference2025 IEEE Conference on Computational Imaging Using Synthetic Apertures, CISA 2025
Country/TerritoryUnited States
CityCollege Park
Period6/2/256/6/25

Keywords

  • Sub-Terahertz
  • Surface Implicit Representations
  • Synthetic Aperture Radar

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Computer Vision and Pattern Recognition
  • Instrumentation

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