LOIND: An illumination and scale invariant RGB-D descriptor
Guanghua Feng, Yong Liu, Yiyi Liao
Abstract
We introduce a novel RGB-D descriptor called local ordinal intensity and normal descriptor (LOIND) with the integration of texture information in RGB image and geometric information in depth image. We implement the descriptor with a 3-D histogram supported by orders of intensities and angles between normal vectors, in addition with the spatial sub-divisions. The former ordering information which is invariant under the transformation of illumination, scale and rotation provides the robustness of our descriptor, while the latter spatial distribution provides higher information capacity so that the discriminative performance is promoted. Comparable experiments with the state-of-art descriptors, e.g. SIFT, SURF, CSHOT and BRAND, show the effectiveness of our LOIND to the complex illumination changes and scale transformation. We also provide a new method to estimate the dominant orientation with only the geometric information, which can ensure the rotation invariance under extremely poor illumination.
BibTeX
@inproceedings{icra2015_loindanilluminat,
title = {LOIND: An illumination and scale invariant RGB-D descriptor},
author = {Guanghua Feng and Yong Liu and Yiyi Liao},
booktitle = {ICRA 2015},
year = {2015}
}