Abstract
We propose a novel keypoint detector for 3D RGB Point Clouds (PCs). The proposed keypoint detector exploits both the 3D structure and the RGB information of the PC data. Keypoint candidates are generated by computing the eigenvalues of the covariance matrix of the PC structure information. Additionally, from the RGB information, we estimate the salient points by an efficient adaptive difference of Gaussian-based operator. Finally, we fuse the resulting two sets of salient points to improve the repeatability of the 3D keypoint detector. The proposed algorithm is compared against the state-of-the-art algorithms on two benchmark datasets. The experimental results show that the proposed scheme outperforms the best existing method by 5.35% and 60.98 points on the SHOT-Kinect dataset and by 5.45% and 145.54 points on the SHOT-SpaceTime dataset in terms of relative and absolute repeatability, respectively.
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