Feature-Preserving Simplification of Point Cloud by Using Clustering Approach Based on Mean Curvature

Abstract

For point cloud data obtained from 3D scanning devices, excessively large storage and long postprocessing time are required. Due to this, it is very important to simplify the point cloud to reduce calculation cost. In this paper, we propose a new point cloud simplification method that can maintain the characteristics of surface shape for unstructured point clouds. In our method, a segmentation range based on mean curvature of point cloud can be controlled. The simplification process is completed by maintaining the position of the representative point and removing the represented points using the range. Our method can simplify results with highly simplified rate with preserving the form feature. Applying the proposed method to 3D stone tool models, the method is evaluated precisely and effectively.

A New Method of Refitting Mixture Lithic Materials by Geometric Matching of Flake Surfaces

“…Matching of the original point clouds requires a large amount of computing time due to the large number of points, which makes the simplification process necessary. The point cloud simplification based on curvature [9] is employed to simplify the flake surfaces. Matching will fail if the features of adjacent flake surfaces are changed by simplification.…”

“…Thus, matching method described in [8] cannot be applied for mixture lithic materials. While the simplification method [9] in this research can obtain simplification results in the same evaluation value for all flake surfaces, the minimal simplified errors are maintained between the original point clouds and the simplified ones by computing the same normalized distance as matching evaluation. Depending on favorable segmentation and simplification results, lithic materials can be refitted with mixture materials of several groups at once.…”