Three-dimensional reconstruction of real objects has always been a hot topic in computer graphics

computer vision

and other fields. In response to the 3D reconstruction of an object obtained with non-uniform rotating from non-fixed angle

a registration method to reconstruct an object 3D model based on RGB-D data is presented by using a rotating platform. Initially

the depth data and color data of the target on the rotating platform are collected by Kinect

and the bounding box algorithm is used to remove the background noise and external points to obtain the required point cloud data with color information. Subsequently

we use point cloud data calibration in different angles to calibrate the center axis of the rotating platform

thereby obtaining the relative relationship between Kinect and the rotating platform. Next

the curvature feature of the target point cloud is used to extract the feature points and find the corresponding points of adjacent point cloud

and then the feature points in non-overlapping regions. For the selection of feature points

we first use k-d tree to search for k neighboring points at any point in the point cloud

apply the surface fitting to these points

and then calculate the Gaussian curvature. The n points of the Gaussian curvature are larger as the characteristic points of the point cloud. The value of n is determined by the number of points

the density

and the complexity of the point cloud. Therefore

n points can reflect the approximate contours or surface features of the object. The Euclidean distance must be considered when selecting corresponding points to better reflect the rotation corresponding relationship of a pair of points in the point cloud in the process of rotation. In the actual registration

a large number of wrong correspondence points are frequently found using Euclidean distance because the point cloud overlap or points are too far away. The use of the arc minimum distance to find the corresponding point can effectively reduce the false point pairs because the target is rotated only around the axis of rotation during the scanning process. Third

the dichotomy iterations method is introduced to find the optimal rotation angle around a central axis

which meets the minimum of the registration error between the two points cloud. Finally

take the point cloud data acquired from any angle to obtain registration under the unified coordinate system and rebuild the model. The experimental results with Stanford University's point cloud database and self-collected database were compared with the existing methods. Compared with the traditional ICP algorithm and the improved ICP algorithm on the Stanford's point cloud database with an average of 75 000 sampling points

the iterative times are reduced by 86.5% and 57.5% respectively

and the running time of the two algorithms are reduced by 87% and 60.75% respectively. The mean square of the Euclidean distance error of the two algorithms is reduced by 70% and 22% respectively. Compared with the traditional ICP and the improved ICP algorithms on the self-collected database with an average of 57 000 sampling points

the average number of iterations is reduced by 94% and 75%

the running time of the algorithm is reduced by 92% and 69%

and the average mean squared of the Euclidean distance error is reduced by 61.5% and 30.6%. Experimental results show that the proposed method is more efficient and has less registration error. Compared with KinectFusion algorithm

this method also has good effects on texture detail preservation. In this paper

the registration algorithm of point cloud based on rotating platform calibration is proposed

and the bisection iterative algorithm can effectively reduce the registration complexity. The comparison of the proposed algorithm and the traditional ICP and improved ICP algorithms also shows the effectiveness of the proposed algorithm. In addition

the superiority of this method is verified by comparing it with other methods in point cloud registration experiments with texture. In this method

only a single Kinect can be used to understand the 3-D modeling of non-uniform rotating objects with non-uniform rotation angles. This method is convenient

practical

and suitable for simple and rapid 3-D reconstruction applications.