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Tuesday, June 21, 2022

Scanned Objects by Google

We looked at common things as we constructed a 'world' to use.   This looks useful, a place to start.   For robotics and beyond.    Lots of links in the below to examples.

Scanned Objects by Google Research: A Dataset of 3D-Scanned Common Household Items

Tuesday, June 14, 2022  in Google Blog

Posted by Laura Downs and Anthony Francis, Software Engineers, Robotics at Google

Many recent advances in computer vision and robotics rely on deep learning, but training deep learning models requires a wide variety of data to generalize to new scenarios. Historically, deep learning for computer vision has relied on datasets with millions of items that were gathered by web scraping, examples of which include ImageNet, Open Images, YouTube-8M, and COCO. However, the process of creating these datasets can be labor-intensive, and can still exhibit labeling errors that can distort the perception of progress. Furthermore, this strategy does not readily generalize to arbitrary three-dimensional shapes or real-world robotic data.

Real-world robotic data collection is very useful, but difficult to scale and challenging to label (figure from BC-Z).

Simulating robots and environments using tools such as Gazebo, MuJoCo, and Unity can mitigate many of the inherent limitations in these datasets. However, simulation is only an approximation of reality — handcrafted models built from polygons and primitives often correspond poorly to real objects. Even if a scene is built directly from a 3D scan of a real environment, the movable objects in that scan will act like fixed background scenery and will not respond the way real-world objects would. Due to these challenges, there are few large libraries with high-quality models of 3D objects that can be incorporated into physical and visual simulations to provide the variety needed for deep learning.

In “Google Scanned Objects: A High-Quality Dataset of 3D Scanned Household Items”, presented at ICRA 2022, we describe our efforts to address this need by creating the Scanned Objects dataset, a curated collection of over 1000 3D-scanned common household items. The Scanned Objects dataset is usable in tools that read Simulation Description Format (SDF) models, including the Gazebo and PyBullet robotics simulators. Scanned Objects is hosted on Open Robotics, an open-source hosting environment for models compatible with the Gazebo simulator.  .... ' 

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