RGBD object pose recognition using local-global multi-kernel regression

The advent of inexpensive depth augmented color (RGBD) sensors has brought about a large advancement in the perceptual capability of vision systems and mobile robots. Challenging vision problems like object category, instance and pose recognition have all benefited from this recent technological advancement. In this paper we address the challenging problem of pose recognition using simultaneous color and depth information. For this purpose, we extend a state-of-the-art regression framework by using a multi-kernel approach to incorporate depth information to perform more effective pose recognition on table-top objects. We do extensive experiments on a large publicly available dataset to validate our approach. We show significant performance improvements (more than 20%) over published results.