Recognizing point clouds using conditional random fields
Cita com:
hdl:2117/26270
Tipus de documentText en actes de congrés
Data publicació2014
EditorInstitute of Electrical and Electronics Engineers (IEEE)
Condicions d'accésAccés obert
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continguts d'aquesta obra estan subjectes a la llicència de Creative Commons
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Reconeixement-NoComercial-SenseObraDerivada 3.0 Espanya
ProjecteINTELLACT - Intelligent observation and execution of Actions and manipulations (EC-FP7-269959)
Abstract
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.
CitacióHusain, S.; Dellen, B.; Torras, C. Recognizing point clouds using conditional random fields. A: International Conference on Pattern Recognition. "Proceedings of the 22nd International Conference on Pattern Recognition". Stockhom: Institute of Electrical and Electronics Engineers (IEEE), 2014, p. 4257-4262.
Versió de l'editorhttp://dx.doi.org/10.1109/ICPR.2014.730
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