RISAS, A Novel Rotation, Illumination, Scale invariant Appearance and Shape Feature
This is a joint work by Kanzhi Wu, Xiaoyang Li, Yong Liu, Ravindra Ranasinghe, Gamini Dissanayake and Rong Xiong and submitted to The 2016 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2016).
Compared with existing prominent 2D image features such as SIFT and SURF, a major limitation of current RGB-D detectors and descriptions is that they are designed without simultaneously considering effectiveness and both detection and description, which the present paper aims to address.
On descriptor end, we further improve previous work, LOIND, into LOIND+ on 2 aspects: 1) more accurate neighbourhood region selection 2) more robust and efficient dominant orientation estimation. On the detector end, based on the design principle of LOIND, we propose an RGB-D keypoint detector which is able to extract information rich region in both grayscale and depth channel. The combination of the proposed detector and descriptor, RISAS, shows state-of-the-art performance under illumination, rotation and scale variations. Furthermore, another contribution of this work is that we construct the first dataset specifically for RGB-D feature evaluation which can be download from link.
C++ code is still under preparing
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