Yale-CMU-Berkeley (YCB) Object and Model Set

Description

This project primarily aims to facilitate performance benchmarking in robotics research. The dataset provides mesh models, RGB, RGB-D and point cloud images of over 80 objects. The physical objects are also available via the YCB benchmarking project. The data are collected by two state of the art systems: UC Berkley's scanning rig and the Google scanner. The UC Berkley's scanning rig data provide meshes generated with Poisson reconstruction, meshes generated with volumetric range image integration, textured versions of both meshes, Kinbody files for using the meshes with OpenRAVE, 600 High-resolution RGB images, 600 RGB-D images, and 600 point cloud images for each object. The Google scanner data provides 3 meshes with different resolutions (16k, 64k, and 512k polygons), textured versions of each mesh, Kinbody files for using the meshes with OpenRAVE.

Update Frequency

Yearly

License

Creative Commons Attribution 4.0 International (CC BY 4.0)

Documentation

http://www.ycbbenchmarks.com/

Managed By

Yale University and Berkeley

See all datasets managed by Yale University and Berkeley.

Contact

bcalli@wpi.edu

How to Cite

Yale-CMU-Berkeley (YCB) Object and Model Set was accessed on DATE from https://registry.opendata.aws/ycb-benchmarks.

Usage Examples

Publications

• Pre-touch sensing for sequential manipulation by Boling Yang, Patrick Lancaster, Joshua R. Smith
  
• The Closure Signature: A Functional Approach to Model Underactuated Compliant Robotic Hands by Maria Pozzi, Gionata Salvietti, João Bimbo, Monica Malvezzi, Domenico Prattichizzo
  
• Label Fusion: A Pipeline for Generating Ground Truth Labels for Real RGBD Data of Cluttered Scenes by Pat Marion, Peter R. Florence, Lucas Manuelli, Russ Tedrake
  
• Benchmarking in Manipulation Research: Using the Yale-CMU-Berkeley Object and Model Set by Berk Calli, Aaron Walsman, Arjun Singh, Siddhartha Srinivasa, Pieter Abbeel, Aaron M Dollar