A Scalable Tree-Based Approach for Joint Object and Pose Recognition

Authors

  • Kevin Lai University of Washington
  • Liefeng Bo University of Washington
  • Xiaofeng Ren Intel Labs
  • Dieter Fox University of Washington

Abstract

Recognizing possibly thousands of objects is a crucial capability for an autonomous agent to understand and interact with everyday environments. Practical object recognition comes in multiple forms: Is this a coffee mug (category recognition). Is this Alice's coffee mug? (instance recognition). Is the mug with the handle facing left or right? (pose recognition). We present a scalable framework, Object-Pose Tree, which efficiently organizes data into a semantically structured tree. The tree structure enables both scalable training and testing, allowing us to solve recognition over thousands of object poses in near real-time. Moreover, by simultaneously optimizing all three tasks, our approach outperforms standard nearest neighbor and 1-vs-all classifications, with large improvements on pose recognition. We evaluate the proposed technique on a dataset of 300 household objects collected using a Kinect-style 3D camera. Experiments demonstrate that our system achieves robust and efficient object category, instance, and pose recognition on challenging everyday objects.

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Published

2011-08-04

How to Cite

Lai, K., Bo, L., Ren, X., & Fox, D. (2011). A Scalable Tree-Based Approach for Joint Object and Pose Recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 25(1), 1474-1480. Retrieved from https://ojs.aaai.org/index.php/AAAI/article/view/7986