iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications

Andrew R. Willis; Kevin M. Brink

doi:10.1117/12.2225540

1 June 2016 iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications

Andrew R. Willis, Kevin M. Brink

Author Affiliations +

Proceedings Volume 9867, Three-Dimensional Imaging, Visualization, and Display 2016; 98670P (2016) https://doi.org/10.1117/12.2225540
Event: SPIE Commercial + Scientific Sensing and Imaging, 2016, Baltimore, MD, United States

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.

Citation Download Citation

Andrew R. Willis and Kevin M. Brink "iGRaND: an invariant frame for RGBD sensor feature detection and descriptor extraction with applications", Proc. SPIE 9867, Three-Dimensional Imaging, Visualization, and Display 2016, 98670P (1 June 2016); https://doi.org/10.1117/12.2225540

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