Clustering-Based Plane Refitting of Non-planar Patches for Voxel-Based 3D Point Cloud Segmentation Using K-Means Clustering

Abstract

Point cloud processing is a struggled field because the points in the clouds are three-dimensional and irregular distributed signals. For this reason, the points in the point clouds are mostly sampled into regularly distributed voxels in the literature. Voxelization as a pretreatment significantly accelerates the process of segmenting surfaces. The geometric cues such as plane directions (normals) in the voxels are mostly used to segment the local surfaces. However, the sampling process may include a non-planar point group (patch), which is mostly on the edges and corners, in a voxel. These voxels can cause misleading the segmentation process. In this paper, we separate the non-planar patches into planar sub-patches using k-means clustering. The largest one among the planar sub-patches replaces the normal and barycenter properties of the voxel with those of itself. We have tested this process in a successful point cloud segmentation method and measure the effects of the proposed method on two point cloud segmentation datasets (Mosque and Train Station). The method increases the accuracy success of the Mosque dataset from 83.84% to 87.86% and that of the Train Station dataset from 85.36% to 87.07%.

[Retracted] A Multiobjective Optimization Model for Prevention and Control of Coronavirus Disease 2019 in China

“…en, the k-means clustering (KMC) model [12][13][14] was introduced to optimize the delivery sites of vaccines and medical staff. Finally, a dynamic material delivery optimization model was constructed to deliver sufficient vaccines and medical staff in the shortest time and at the minimum cost.…”

RANSAC-Based Planar Point Cloud Segmentation Enhanced by Normal Vector and Maximum Principal Curvature Clustering

“…For example, Xu et al (2019) employed an improved density clustering algorithm to group points together, facilitating the identification and fitting of planes in the point cloud data. Sağlam et al (2020) segmented the point cloud data into patches using K-means clustering and then refining these nonplanar patches into plane segments. These methods typically cluster points based on either spatial proximity or normal vectors at a time, often requiring a secondary segmentation step.…”

The Extraction of Roof Feature Lines of Traditional Chinese Village Buildings Based on UAV Dense Matching Point Clouds

“…This involves several phases, including segmenting the point cloud data related to roofs, fitting planes, extracting contours, and ensuring regularity. Several techniques are employed, such as the Hough transform [32,33], regiongrowing algorithm [34,35], clustering method [36,37], and RANSAC [38,39]. However, these algorithms frequently and significantly depend on normal vectors and curvature thresholds, which may lead to false planes and threshold sensitivity [40].…”