NeAT: Neural Adaptive Tomography

Darius Rückert, Yuanhao Wang, Rui Li, Ramzi Idoughi, Wolfgang Heidrich

Research output: Contribution to journalArticlepeer-review

16 Scopus citations

Abstract

In this paper, we present Neural Adaptive Tomography (NeAT), the first adaptive, hierarchical neural rendering pipeline for tomography. Through a combination of neural features with an adaptive explicit representation, we achieve reconstruction times far superior to existing neural inverse rendering methods. The adaptive explicit representation improves efficiency by facilitating empty space culling and concentrating samples in complex regions, while the neural features act as a neural regularizer for the 3D reconstruction. The NeAT framework is designed specifically for the tomographic setting, which consists only of semi-transparent volumetric scenes instead of opaque objects. In this setting, NeAT outperforms the quality of existing optimization-based tomography solvers while being substantially faster.

Original languageEnglish (US)
Article number55
JournalACM transactions on graphics
Volume41
Issue number4
DOIs
StatePublished - Jul 22 2022

Bibliographical note

Publisher Copyright:
© 2022 Association for Computing Machinery.

Keywords

  • Implicit neural representation
  • Octree
  • X-ray computed tomography

ASJC Scopus subject areas

  • Computer Graphics and Computer-Aided Design

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