Point-Cloud Data Reduction Based on Neighborhood-Point Position Feature

Massive point -cloud data involve considerable difficulties in storage, transmission, and processing. To address the problem that existing algorithms cannot consider the surface area, volume, or reconstruction error of the reconstructed model after feature preservation and simplification, we propose a point -cloud simplification algorithm based on the location features of neighboring points. The algorithm simplifies the target point -cloud according to the weight calculation projection plane, search matrix size, and reduction ratio. To mesh the target point -cloud, we find the vertical direction of the projection plane (positive and negative directions), take the target point as the center, and obtain the points within the search matrix. The weight value is determined according to the position relationship between the point in the search matrix and the target point, and the original point -cloud is reduced according to the reduction ratio set. The proposed algorithm is compared with curvature sampling, uniform grid, and random sampling methods, and is evaluated in terms of feature retention, surface area, and rate of change of volume. Experimental results show that the reductions performed by the proposed algorithm are better than those provided by the uniform grid and the random sampling methods for feature regions, and are consistent with the curvature sampling method. The reduction causes the error, surface product difference, and volume difference of the reconstructed model to be generally superior to those of the curvature sampling method, consistent with the random sampling method, and slightly inferior to those of the uniform grid method. Therefore, the proposed algorithm not only preserves features, but also reduces the variation and error in the surface area and volume of the reconstructed model.