TY - JOUR AU - Lai, Kevin AU - Bo, Liefeng AU - Ren, Xiaofeng AU - Fox, Dieter PY - 2011/08/04 Y2 - 2024/03/28 TI - A Scalable Tree-Based Approach for Joint Object and Pose Recognition JF - Proceedings of the AAAI Conference on Artificial Intelligence JA - AAAI VL - 25 IS - 1 SE - Physically Grounded AI Special Track DO - 10.1609/aaai.v25i1.7986 UR - https://ojs.aaai.org/index.php/AAAI/article/view/7986 SP - 1474-1480 AB - <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> ER -