ICRA 2011 Paper Abstract

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Paper TuP1-InteracInterac.28

Lai, Kevin (University of Washington), Bo, Liefeng (University of Washington), Ren, Xiaofeng (Intel Labs Seattle), Fox, Dieter (University of Washington)

A Large-Scale Multi-View RGB-D Object Dataset

Scheduled for presentation during the Poster Sessions "Interactive Session II: Systems, Control and Automation" (TuP1-InteracInterac), Tuesday, May 10, 2011, 13:40−14:55, Hall

2011 IEEE International Conference on Robotics and Automation, May 9-13, 2011, Shanghai International Conference Center, Shanghai, China

This information is tentative and subject to change. Compiled on March 30, 2020

Keywords Recognition, Computer Vision for Robotics and Automation

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 (depth) camera. With its advanced sensing capabilities and the potential for mass production, this technology represents an opportunity to dramatically increase robotics 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.

 

 

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