Publication

FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation

Conference Article

Conference

IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)

Edition

2013

Pages

824-830

Doc link

http://dx.doi.org/10.1109/IROS.2013.6696446

File

Download the digital copy of the doc pdf document

Abstract

Most current depth sensors provide 2.5D range images in which depth values are assigned to a rectangular 2D array. In this paper we take advantage of this structured information to build an efficient shape descriptor which is about two orders of magnitude faster than competing approaches, while showing similar performance in several tasks involving deformable object recognition. Given a 2D patch surrounding a point and its associated depth values, we build the descriptor for that point, based on the cumulative distances between their normals and a discrete set of normal directions. This processing is made very efficient using integral images, even allowing to compute descriptors for every range image pixel in a few seconds. The discriminative power of our descriptor, dubbed FINDDD, is evaluated in three different scenarios: recognition of specific cloth wrinkles, instance recognition from geometry alone, and detection of reliable and informed grasping points.



Categories

computer vision, feature extraction.

Author keywords

3D descriptor, range image, real-time features

Scientific reference

A. Ramisa, G. Alenyà, F. Moreno-Noguer and C. Torras. FINDDD: A fast 3D descriptor to characterize textiles for robot manipulation, 2013 IEEE/RSJ International Conference on Intelligent Robots and Systems, 2013, Tokyo, pp. 824-830.