research-article

NeAT: neural adaptive tomography

Authors:

Darius Rückert,

Wolfgang HeidrichAuthors Info & Claims

ACM Transactions on Graphics (TOG), Volume 41, Issue 4

Article No.: 55, Pages 1 - 13

https://doi.org/10.1145/3528223.3530121

Published: 22 July 2022 Publication History

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.

https://github.com/darglein/NeAT

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Index Terms

NeAT: neural adaptive tomography
1. Computing methodologies

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cover image ACM Transactions on Graphics

ACM Transactions on Graphics Volume 41, Issue 4

July 2022

1978 pages

ISSN:0730-0301

EISSN:1557-7368

DOI:10.1145/3528223

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Published: 22 July 2022

Published in TOG Volume 41, Issue 4

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