| Title | A Scalable Tree-based Approach for Joint Object and Pose Recognition |
| Publication Type | Conference Paper |
| Year of Publication | 2011 |
| Authors | Lai K, Bo L, Ren X, Fox D |
| Conference Name | AAAI |
| Abstract | <p>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.</p> |
| Downloads | http://www.cs.washington.edu/ai/Mobile_Robotics/postscripts/scalable-rec... PDF |
| Citation Key | Lai11Sca |
