Targeting at 3D point cloud data without any foreknowledge of information, this paper presents a new algorithm of point cloud simplification. Because of usual way of shooting in daily life, there often exist more detailed information in x-y direction in the point cloud. By using this feature, the proposed algorithm firstly selects x-y axis as the direction for division and computation and obtains x-y boundary. After observation of normal vector of point cloud, it is easy to find that if the normal vector of the points in the local region changes gently, it indicates that the region is relatively flat. On the contrary, if the normal vector changes greatly, it indicates that the region fluctuates greatly. Therefore, compute the arithmetic mean of the included angle between the normal vector of one point in the point cloud and the normal vector of its k-neighborhood point. Define the feature of that point, and based on this, extract key feature points in data. Finally, the gridding method is used to divide the scattered point cloud data whose boundary and key points have been extracted and thus finish simplification. Experimental results show the effectiveness of the proposed algorithm.

Targeting at prerequisites in the process of registering 3D point cloud data under different angles of view by ICP algorithm, a new automatic coarse registration algorithm is presented in this paper. Firstly, through computing normal vector variation of point cloud within different radius, simplify point cloud and keep necessary key points. Then comprehensively use three geometric features of key points in point clouds to search for matching point pairs. Next, use ZNCC (zero-mean normalized cross-correlation coefficient) registration criterion which is used in 2D image matching, so as to compute neighborhood similarity between matching point pairs. According to neighborhood similarity, build one-to-one correspondence between point pairs and establish coarse registration point pairs afterwards and finally get coarse matching parameters. The experimental results show that either used alone or combined with ICP algorithm, this algorithm is a great improvement in comparison to solely using ICP algorithm or other improved ICP algorithms in terms of efficiency and accuracy.

An octree-based point cloud compression algorithm is proposed from 3D point cloud data that are without any foreknowledge of information. The algorithm improves the stop condition of segmentation to stop dividing at the right depth and to ensure appropriate voxel size; and in the data structure, each node is assigned a bitmask; by manipulating bitmask, query and remove data when traversing; later position encode the points and thus effectively remove the outliers and surface noise, increase point cloud compression efficiency with range encoding. Experimental results show that the algorithm much completely retains key information of point cloud, and has some theoretical significance and application value for point cloud data preprocessing.