A Large-Scale Hierarchical Multi-View RGB-D Object Dataset
Proc. of International Conference on Robotics and Automation (ICRA), 2011
Abstract
Over the last decade, the availability of public
image repositories and recognition benchmarks has enabled
rapid progress in visual object category and instance detection.
Today we are witnessing the birth of a new generation of
sensing technologies capable of providing high quality synchronized
videos of both color and depth, the RGB-D (Kinectstyle)
camera. With its advanced sensing capabilities and the
potential for mass adoption, this technology represents an
opportunity to dramatically increase robotic object recognition,
manipulation, navigation, and interaction capabilities. In this
paper, we introduce a large-scale, hierarchical multi-view object
dataset collected using an RGB-D camera. The dataset
contains 300 objects organized into 51 categories and has been
made publicly available to the research community so as to
enable rapid progress based on this promising technology. This
paper describes the dataset collection procedure and introduces
techniques for RGB-D based object recognition and detection,
demonstrating that combining color and depth information
substantially improves quality of results.