ORIGINAL RESEARCH article
Front. Phys.
Sec. Optics and Photonics
This article is part of the Research TopicAcquisition and Application of Multimodal Sensing Information, Volume IIIView all 6 articles
LiDAR Point Cloud Down-sampling Strategy with MultiDimensional Membership Fusion
Provisionally accepted
- 1Xi'an Peihua University, Xi'an, China
- 2Xi'an Institute of Applied Optics, Xi'an, China
- 3Xidian University, Xi'an, China
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This study devises an innovative LiDAR point cloud down-sampling strategy that capitalizes on the properties of Fuzzy C Means (FCM) clustering membership functions in each dimension. Traditional down-sampling methods frequently encounter difficulties in striking a balance between computational efficiency and feature preservation, particularly for large-scale datasets. To tackle this issue, our approach breaks down the three-dimensional simplification problem into independent one-dimensional analyses. Specifically, FCM clustering is carried out separately on the X, Y, and Z coordinates to generate dimension-wise membership functions. These functions are then intelligently integrated to calculate comprehensive importance scores for each point, facilitating adaptive sampling that eliminates redundant data while retaining critical geometric features. Experimental results demonstrate that our method outperforms conventional approaches, including voxel grid, random, and farthest point sampling, in terms of geometric fidelity. The proposed method shows strong potential for real-time applications involving large-scale point clouds in fields such as autonomous driving, robotic navigation, and 3D reconstruction.
Keywords: Adaptive sampling, FCM clustering, Feature preservation, Light detection, Membership Fusion, Point Cloud Down-sampling
Received: 04 Jan 2026; Accepted: 04 Feb 2026.
Copyright: © 2026 Cao, Feng and Mao. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Weiwei Mao
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