3D Keypoints Detection from a 3D Point Cloud for Real-Time Camera Tracking

Abstract

In this paper we propose a method for detecting 3D keypoints in a 3D point cloud for robust real-time camera tracking. Assuming that there are a number of images corresponding to the 3D point cloud, we define a 3D keypoint as a point that has corresponding 2D keypoints in many images. These 3D keypoints are expected to appear with high probability as 2D keypoints in newly taken query images. For 3D-2D matching, we embed 2D feature descriptors into the 3D keypoints. Experimental results with 3D point clouds of indoor and outdoor scenes show that the extracted 3D keypoints can be used for matching with 2D keypoints in query images.