Approximating Human-Level 3D Visual Inferences With Deep Neural Networks

Humans make rich inferences about the geometry of the visual world. While deep neural networks (DNNs) achieve human-level performance on some psychophysical tasks (e.g., rapid classification of object or scene categories), they often fail in tasks requiring inferences about the underlying shape of o...

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Bibliographic Details

Published in:	Open mind (Cambridge, Mass.) Vol. 9; pp. 305 - 324
Main Authors:	O’Connell, Thomas P., Bonnen, Tyler, Friedman, Yoni, Tewari, Ayush, Sitzmann, Vincent, Tenenbaum, Joshua B., Kanwisher, Nancy
Format:	Journal Article
Language:	English
Published:	255 Main Street, 9th Floor, Cambridge, Massachusetts 02142, USA MIT Press 16.02.2025
Subjects:	3D shape perception deep neural networks neural fields psychophysics 3D shape perception psychophysics neural fields deep neural networks
ISSN:	2470-2986, 2470-2986
Online Access:	Get full text
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