Fuzzy logic and histogram of normal orientation-based 3D keypoint detection for point clouds

Abstract Point cloud processing has gained consideration for 3D object recognition and classification tasks. In this context, an important task is to detect the distinct and repeatable 3D keypoints. Many 3D keypoint detectors with low repeatability and distinctiveness have been proposed. The detection of highly repeatable and distinct keypoints is still an open problem. To address this issue, we propose a fuzzy logic and Histogram of Normal Orientation (HoNO)-based 3D keypoint detection scheme for Point Cloud (PC) data. To measure saliency, we exploit the structure of the PC and compute the eigenvalues of the covariance matrix and the HoNO to measure saliency. The histogram (HoNO) salient value is computed by the kurtosis values, which estimate the spread of the histogram. From the kurtosis and smallest eigenvalues, we compute the difference of the kurtosis values and the difference of the smallest eigenvalues of the query point against all the neighbouring points. The difference of kurtosis values and difference of smallest eigenvalues are applied to a fuzzy rule-based scheme for the keypoints detection. We compare the proposed algorithm with the state-of-the-art 3D keypoint detectors on five benchmark datasets. Experimental results demonstrate the superior performance of the proposed detector on most of the benchmark datasets both in terms of absolute and relative repeatability.