  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.
