A convolutional learning system for object classification in 3-D Lidar data.
In this brief, a convolutional learning system for classification of segmented objects represented in 3-D as point clouds of laser reflections is proposed. Several novelties are discussed: (1) extension of the existing convolutional neural network (CNN) framework to direct processing of 3-D data in a multiview setting which may be helpful for rotation-invariant consideration, (2) improvement of CNN training effectiveness by employing a stochastic meta-descent (SMD) method, and (3) combination of unsupervised and supervised training for enhanced performance of CNN. CNN performance is illustrated on a two-class data set of objects in a segmented outdoor environment.
Prokhorov
Danil
D
Toyota Research Instituje NA, Ann Arbor, MI 48105, USA. dvprokhorov@gmail.com
D016000:Cluster Analysis; D016208:Databases, Factual / Q000706:statistics & numerical data; D006801:Humans; D016247:Information Storage and Retrieval / Q000379:methods*; D007858:Learning / Q000502:physiology*; D016571:Neural Networks, Computer