Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items
Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items
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DOI：
10.48550/arxiv.2204.11918
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发表时间：
2022-04
期刊：
影响因子：
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通讯作者：
Laura Downs;Anthony Francis;Nate Koenig;Brandon Kinman;R. Hickman;Krista Reymann;T. B. McHugh;Vincent Vanhoucke
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文献类型：
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作者：
Laura Downs;Anthony Francis;Nate Koenig;Brandon Kinman;R. Hickman;Krista Reymann;T. B. McHugh;Vincent Vanhoucke
交互式3D模拟已经在机器人和计算机视觉中实现了突破性进展，但模拟深度学习所需的各种环境需要大量照片般逼真的3D对象模型。为了满足这一需求，我们推出了Google Scanned Objects，这是一个开源的集合，包含了1000多个3D扫描的家用物品，根据知识共享许可证发布;这些模型经过预处理，可用于Ignition Gazebo和Bullet模拟平台，但很容易适应其他模拟器。我们描述了我们的对象扫描和策展管道，然后提供有关数据集内容及其使用情况的统计数据。我们希望Google Scanned Objects的多样性、质量和灵活性将推动交互式仿真、合成感知和机器人学习的进步。
Interactive 3D simulations have enabled break-throughs 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.