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In this paper, we propose two methods for estimating the scales of point clouds to align them. The first method estimates the scale of each point cloud separately: each point cloud has its own scale that is something like the size of a scene. We call it a keyscale, which is a representative scale and is defined for a given 3D point cloud as the minimum of(More)
This paper describes a method for calibrating non-overlapping cameras in a simple way: using markers on the cameras. By adding an AR (Augmented Reality) marker to a camera, we can find the transformation between the fixed AR marker and the camera's center. With such information, relative pose of cameras can be easily found as long as the marker located on(More)
In this paper, we propose a method for detecting Scale-Invariant Point Feature(SIPF) including 3D keypoints Detector and feature descriptor. To detect SIPF, we first estimate a keyscale for point cloud, and calculate the covariance matrix of each 3D point. Keypoints are the saliency points who have a fast change speed along with all principal directions.(More)
3D point clouds are important for the reconstruction of environment. However, comparing to the artificial VR scene representation methods, 3D point clouds are more difficult to correspond to real scenes. In this paper, a method for detecting keypoints and describing scale invariant point feature of 3D point clouds is proposed. To detect, we first select(More)
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