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Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items

Anthony G. Francis
Brandon Kinman
Laura Downs
Nathan Koenig
Ryan M. Hickman
Thomas B. McHugh
Google Scholar


Interactive 3D simulations have enabled breakthroughs in robotics and computer vision, but simulating the broad diversity of environments needed for deep learning requires large corpora of photo-realistic 3D object models. To address this need, we present Google Scanned Objects, an open-source collection of over one thousand 3D-scanned household items; these models are preprocessed for use in Ignition Gazebo and the Bullet simulation platforms, but are easily adaptable to other simulators. We describe our object scanning and curation pipeline, then provide statistics about the contents of the dataset and its usage. We hope that the diversity, quality, and flexibility that Google Scanned Objects provides will lead to further advances in interactive simulation, synthetic perception, and robotic learning.

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