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Proceedings Paper

iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications
Author(s): Andrew R. Willis; Kevin M. Brink
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Paper Abstract

This article describes a new 3D RGBD image feature, referred to as iGRaND, for use in real-time systems that use these sensors for tracking, motion capture, or robotic vision applications. iGRaND features use a novel local reference frame derived from the image gradient and depth normal (hence iGRaND) that is invariant to scale and viewpoint for Lambertian surfaces. Using this reference frame, Euclidean invariant feature components are computed at keypoints which fuse local geometric shape information with surface appearance information. The performance of the feature for real-time odometry is analyzed and its computational complexity and accuracy is compared with leading alternative 3D features.

Paper Details

Date Published: 1 June 2016
PDF: 15 pages
Proc. SPIE 9867, Three-Dimensional Imaging, Visualization, and Display 2016, 98670P (1 June 2016); doi: 10.1117/12.2225540
Show Author Affiliations
Andrew R. Willis, Univ. of North Carolina at Charlotte (United States)
Kevin M. Brink, Air Force Research Lab. (United States)


Published in SPIE Proceedings Vol. 9867:
Three-Dimensional Imaging, Visualization, and Display 2016
Bahram Javidi; Jung-Young Son, Editor(s)

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