Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items
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 colle...
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| Veröffentlicht in: | arXiv.org 2022-04 |
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| creator | Downs, Laura Francis, Anthony Koenig, Nate Kinman, Brandon Hickman, Ryan Reymann, Krista McHugh, Thomas B Vanhoucke, Vincent |
| description | 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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| subjects | Computer vision Datasets Deep learning Robotics Simulation Simulators Three dimensional models |
| title | Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items |
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