Article: Pose estimation technology of electronic components based on point cloud segmentation algorithm Journal: International Journal of Computer Applications in Technology (IJCAT) 2026 Vol.78 No.2 pp.124 - 134 Abstract: In actual manufacturing environments, electronic components often face occlusion problems, which makes it difficult for traditional point cloud segmentation methods to estimate the pose of objects accurately. To address this challenge, this paper introduces the multi-scale feature learning capability provided by PointNet++ to extract deep collective feature information in local areas of different scales and understand the overall morphology of components in a global context. According to experimental analysis, under the same occlusion level, PointNet++ outperforms the PointNet model, the RANSAC (Random Sample Consensus) algorithm, and the voxelisation method Point-Voxel CNN in terms of segmentation accuracy. The pose estimation method of electronic components studied in this paper is highly applicable in actual mechanical manufacturing environments, can process large-scale data, and meets real-time requirements. It provides the theoretical basis and technical support for solving the positioning and assembly problems of components in actual industrial production. Inderscience Publishers - linking academia, business and industry through research

Title: Pose estimation technology of electronic components based on point cloud segmentation algorithm

Authors: Wei Shen

Addresses: School of Engineering, Nanjing Normal University Zhongbei College, Danyang, Jiangsu, China

Abstract: In actual manufacturing environments, electronic components often face occlusion problems, which makes it difficult for traditional point cloud segmentation methods to estimate the pose of objects accurately. To address this challenge, this paper introduces the multi-scale feature learning capability provided by PointNet++ to extract deep collective feature information in local areas of different scales and understand the overall morphology of components in a global context. According to experimental analysis, under the same occlusion level, PointNet++ outperforms the PointNet model, the RANSAC (Random Sample Consensus) algorithm, and the voxelisation method Point-Voxel CNN in terms of segmentation accuracy. The pose estimation method of electronic components studied in this paper is highly applicable in actual mechanical manufacturing environments, can process large-scale data, and meets real-time requirements. It provides the theoretical basis and technical support for solving the positioning and assembly problems of components in actual industrial production.

Keywords: point cloud segmentation; pose estimation; PointNet++ Model; occlusion problems; mechanical manufacturing; random sample consensus.

DOI: 10.1504/IJCAT.2026.151719

International Journal of Computer Applications in Technology, 2026 Vol.78 No.2, pp.124 - 134

Received: 25 Feb 2025
Accepted: 11 Sep 2025
Published online: 17 Feb 2026 *

Title: Pose estimation technology of electronic components based on point cloud segmentation algorithm

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