Hierarchical Point Saliency for 3D Keypoint Detection

Keypoint detection plays a fundamental role in many applications, such as 3D reconstruction, object registration, and shape retrieval, and has attracted significant interest from researchers in computer vision and graphics. However, due to the ambiguity of the keypoint and the complexity of 3D objects, it is still tricky for existing 3D keypoint detection methods to generate stable keypoints with good coverage, especially for unsupervised detection methods. This paper proposes a 3D keypoint detection method based on hierarchical point saliency. This method can effectively and accurately locate the keypoints of a 3D point cloud, and it does not require complex training processes. First, we propose a simple and effective point descriptor called the local geometric structure feature, which can effectively characterize the geometric structure changes of 3D point clouds and has a strong feature identification ability. Second, we define two saliency measures used to characterize the saliency of points in the point cloud, which are low-level and high-level saliency. Third, we hierarchically characterize the saliency of points by combining the low-level and high-level saliency, thus measuring the probability that a point belongs to a keypoint. Finally, we extensively test our method on three benchmark 3D point cloud datasets, and the experimental results demonstrate that our method achieves state-of-the-art performance in keypoint detection tasks, significantly superior to the prior hand-crafted and deep-learning-based 3D keypoint detection methods.