针对目前三维点云关键点检测方法检测的关键点的可描述性和再现性不强，且检测的关键点数量较少的问题，提出了一种新颖的关键点检测算法。首先为提高算法的效率，利用均匀采样方法减少三维点云中点的数量，降低三维点云的复杂度。然后利用具有良好描述性的方向直方图签名(SHOT)描述子对均匀采样的点进行多尺度描述，分析每个点多尺度SHOT描述子的独特性，选取SHOT描述子离散程度较大的点作为关键点。本文方法利用描述性较强的SHOT描述子对关键点的邻域进行描述，增强了关键点的可描述性。实验结果表明，本文方法的均匀采样时间效率高，满足关键点检测的时间要求，且本文检测关键点的方法比Harris3D、尺度不变特征变换（SIFT）、内部形状签名（ISS）关键点检测算法具有更好的再现性。因此，本文方法可以有效、快速地在三维点云模型和场景中检测出高质量的关键点。

Aiming at the key point description and repeatability in the method of key point detection of three-dimensional (3D) point cloud is not strong, and the number of detected key points is small, we propose a novel algorithm of key point detection. Firstly, in order to improve the efficiency of the algorithm, the uniform sampling method is used to reduce the number of points in the 3D point cloud that can reduce the complexity of the 3D point cloud. Then, we use Signature of Histograms of OrienTations (SHOT) descriptor to describe the points uniformly sampled in multi-scale, and analyze the uniqueness of the multi-scale SHOT descriptors at each point, and select the SHOT descriptor with larger discreteness of points as the key points. The proposed method uses the descriptive SHOT descriptor to describe the neighborhood of the key points, and enhances the descriptivity of the key points. The experimental results show that the uniform sampling is highly efficient in time and meets the time requirements of the keypoint detection. The proposed method has better reproducibility than Harris 3D, scale invariant feature transform (SIFT) and internal shape signature (ISS) key point detection algorithms in the key point detection. Therefore, the proposed method can detect high quality key points in the 3D point cloud model and scene effectively and quickly.