2026-04-13T03:58:06Zhttps://recercat.cat/oai/request

oai:recercat.cat:2117/262702026-01-30T08:18:27Zcom_2072_1033col_2072_452950

00925njm 22002777a 4500 dc Husain, Syed Farzad author Dellen, Babette author Torras, Carme author 2014 Detecting objects in cluttered scenes is a necessary step for many robotic tasks and facilitates the interaction of the robot with its environment. Because of the availability of efficient 3D sensing devices as the Kinect, methods for the recognition of objects in 3D point clouds have gained importance during the last years. In this paper, we propose a new supervised learning approach for the recognition of objects from 3D point clouds using Conditional Random Fields, a type of discriminative, undirected probabilistic graphical model. The various features and contextual relations of the objects are described by the potential functions in the graph. Our method allows for learning and inference from unorganized point clouds of arbitrary sizes and shows significant benefit in terms of computational speed during prediction when compared to a state-of-the-art approach based on constrained optimization. Peer Reviewed Postprint (author’s final draft) Àrees temàtiques de la UPC::Informàtica::Robòtica computer vision object detection object recognition. Classificació INSPEC::Pattern recognition Recognizing point clouds using conditional random fields