A Point Cloud Simplification Algorithm Based on Weighted Feature Indexes for 3D Scanning Sensors

Abstract

Conventional point cloud simplification algorithms have problems including nonuniform simplification, a deficient reflection of point cloud characteristics, unreasonable weight distribution, and high computational complexity. A simplification algorithm, namely, the multi-index weighting simplification algorithm (MIWSA), is proposed in this paper. First, the point cloud is organized with a bounding box and kd-trees to find the neighborhood of each point, and the points are divided into small segments. Second, the feature index of each point is calculated to indicate the characteristics of the points. Third, the analytic hierarchy process (AHP) and criteria importance through intercriteria correlation (CRITIC) are applied to weight these indexes to determine whether each point is a feature point. Fourth, non-feature points are judged as saved or abandoned according to their spatial relationship with the feature points. To verify the effect of the MIWSA, 3D model scanning datasets are calculated and analyzed, as well as field area scanning datasets. The accuracy for the 3D model scanning datasets is assessed by the surface area and patch numbers of the encapsulated surfaces, and that for field area scanning datasets is evaluated by the DEM error statistics. Compared with existing algorithms, the overall accuracy of the MIWSA is 5% to 15% better. Additionally, the running time is shorter than most. The experimental results illustrate that the MIWSA can simplify point clouds more precisely and uniformly.

A Color- and Geometric-Feature-Based Approach for Denoising Three-Dimensional Cultural Relic Point Clouds

“…Therefore, it is necessary to simplify the raw data to a more appropriate size without affecting the denoising effect. We use the method described in [43] for simplifying point clouds. The simplification process is divided into the following four steps:…”

Fast subsampling strategy for point cloud based on novel octree coding

“…Subsampling according to coded point clouds can effectively improve the feature richness of the subsampling point clouds. Some algorithms are devoted to the study of point cloud subsampling algorithms combining spatial coding and feature point selection, Zhu et al use principal component analysis to set the importance level of points [11], Zhang et al define multiple kinds of entropy including scale retention, contour retention, curvature retention to quantify the point features in order to keep the important feature points [12], Ji et al extract the feature points by calculating the four feature indexes and combining them with the octree [13], and Shi et al organize the point cloud by adopting a KdTree, and use a multi-index weighting approach to compute feature indices of the points in order to keep the feature points [14]. However, the point feature quantization methods of these algorithms are usually computationally complex, and none of them take into account the effect of point cloud noise on feature point selection.…”