Abstract-Consumer depth cameras, such as the Microsoft Kinect, are capable of providing frames of dense depth values at real time. One fundamental question in utilizing depth cameras is how to best extract features from depth frames. Motivated by local descriptors on images, in particular kernel descriptors, we develop a set of kernel features on depth images that model size, 3D shape, and depth edges in a single framework. Through extensive experiments on object recognition, we show that (1) our local features capture different aspects of cues from a depth frame/view that complement one another; (2) our kernel features significantly outperform traditional 3D features (e.g. Spin images); and (3) we significantly improve the capabilities of depth and RGB-D (color+depth) recognition, achieving 10 − 15% improvement in accuracy over the state of the art.
Bo et al. REF developed a set of kernel features on point clouds and depth images which contained size, shape and edge features.
Depth kernel descriptors for object recognition
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
2011 IEEE/RSJ International Conference on Intelligent Robots and Systems
Computer Science