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.

点云处理已成为3D对象识别和分类任务的考虑因素。在这种情况下，一项重要的任务是检测独特且可重复的3D关键点。已经提出了许多具有低重复性和独特性的3D关键点检测器。检测高度可重复且截然不同的关键点仍然是一个未解决的问题。为了解决这个问题，我们提出了一种基于模糊逻辑和直方图直方图的基于HoNO的3D关键点检测方案，用于点云（PC）数据。为了测量显着性，我们利用PC的结构并计算协方差矩阵和HoNO的特征值来测量显着性。直方图（HoNO）显着值由峰度值计算，该峰度值估计了直方图的范围。从峰度和最小特征值来看，我们针对所有相邻点计算峰度值的差异和查询点的最小特征值的差异。峰度值的差异和最小特征值的差异被应用于基于模糊规则的关键点检测方案。我们将提出的算法与五个基准数据集上的最新3D关键点检测器进行了比较。实验结果证明，在绝对和相对重复性方面，建议的检测器在大多数基准数据集上均具有出色的性能。我们将提出的算法与五个基准数据集上的最新3D关键点检测器进行了比较。实验结果证明，在绝对和相对重复性方面，建议的检测器在大多数基准数据集上均具有出色的性能。我们将提出的算法与五个基准数据集上的最新3D关键点检测器进行了比较。实验结果证明，在绝对和相对重复性方面，建议的检测器在大多数基准数据集上均具有出色的性能。