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

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arxiv 2204.11918 v1 pith:R3TYFQFM submitted 2022-04-25 cs.RO cs.GR

Laura Downs , Anthony Francis , Nate Koenig , Brandon Kinman , Ryan Hickman , Krista Reymann , Thomas B. McHugh , Vincent Vanhoucke This is my paper · ORCID
classification cs.ROcs.GR
keywords scannedgoogleobjectsdatasetdiversityhouseholdinteractiveitems
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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 released under a Creative Commons license; 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 of Google Scanned Objects will lead to advances in interactive simulation, synthetic perception, and robotic learning.

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Forward citations

Cited by 6 Pith papers

Reviewed papers in the Pith corpus that reference this work. Sorted by Pith novelty score. OpenAlex reports about 13 citations worldwide. Full citation record

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    cs.RO 2025-02 conditional novelty 6.0 of 10

    COMBO-Grasp trains a stabilizing constraint policy and an RL grasping policy, then refines the constraint pose with value-function gradients, improving bimanual grasping of occluded objects in simulation and real world.

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    Embodied training data form a five-layer pyramid—real-robot, UMI, ego/exo, simulation, general V–L—ordered by the trade-off between scale and robot alignment, and model capabilities track how those layers are mixed.

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