Keypoint detection serves as the basis for many computer vision and robotics applications. Despite the fact that colored point clouds can be readily obtained, most existing keypoint detectors extract only geometry-salient keypoints, which can impede the overall performance of systems that intend to (or have the potential to) leverage color information. To promote advances in such systems, we propose an efficient multi-modal keypoint detector that can extract both geometry-salient and color-salient keypoints in colored point clouds. The proposed CEntroid Distance (CED) keypoint detector comprises an intuitive and effective saliency measure, the centroid distance, that can be used in both 3D space and color space, and a multi-modal non-maximum suppression algorithm that can select keypoints with high saliency in two or more modalities. The proposed saliency measure leverages directly the distribution of points in a local neighborhood and does not require normal estimation or eigenvalue decomposition. We evaluate the proposed method in terms of repeatability and computational efficiency (i.e. running time) against state-of-the-art keypoint detectors on both synthetic and real-world datasets. Results demonstrate that our proposed CED keypoint detector requires minimal computational time while attaining high repeatability. To showcase one of the potential applications of the proposed method, we further investigate the task of colored point cloud registration. Results suggest that our proposed CED detector outperforms state-of-the-art handcrafted and learning-based keypoint detectors in the evaluated scenes. The C++ implementation of the proposed method is made publicly available at https://github.com/UCR-Robotics/CED_Detector.

翻译：关键点检测是许多计算机视觉和机器人应用的基础。尽管彩色点云易于获取，但现有大多数关键点检测器仅提取几何显著关键点，这可能阻碍旨在（或有潜力）利用颜色信息的系统的整体性能。为促进此类系统的发展，我们提出一种高效的多模态关键点检测器，可同时提取彩色点云中的几何显著和颜色显著关键点。所提出的质心距离（CED）关键点检测器包含一种直观有效的显著性度量——质心距离，该度量可同时用于三维空间和颜色空间；以及一种多模态非极大值抑制算法，能够从两个或更多模态中选择高显著性的关键点。所提出的显著性度量直接利用局部邻域内点的分布，无需法向量估计或特征值分解。我们在合成与真实数据集上，就重复性和计算效率（即运行时间）对提出的方法与现有最优关键点检测器进行了评估。结果表明，我们提出的CED关键点检测器在实现高重复性的同时，所需计算时间最少。为展示该方法的一种潜在应用，我们进一步研究了彩色点云配准任务。结果表明，在评估场景中，我们提出的CED检测器在性能上优于现有最优的手工设计及基于学习的关键点检测器。该方法的C++实现已在https://github.com/UCR-Robotics/CED_Detector 开源。