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

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 effi...

• Type: Journal
• Year: 2011
• Where: AAAI
• Authors: Kevin Lai, Liefeng Bo, Xiaofeng Ren, Dieter Fox
• Keywords: AAAI 2011, Everyday Environments, Household Objects, Instance Recognition, Intelligent Agents