Construction of Efficient Thinning Model of Point Cloud Data Based on PCA and Deep Learning

Shiping Yang,Chen Luo, Bin Wang,Xuepeng Mou,Wei Wang

2024 International Conference on Electrical Drives, Power Electronics & Engineering (EDPEE)（2024）

Power grid Planning Research Center of Guizhou Power Grid Co.

Cited 0|Views6

Abstract

Point cloud data is widely used in 3D vision and perception, but its large-scale and high-dimensional characteristics bring challenges to data processing and storage. In order to solve this problem, an efficient point cloud data thinning model is proposed based on principal component analysis(PCA) and deep learning technology. Firstly, the dimensionality reduction combined with PCA can effectively reduce the redundancy of point cloud data, thus reducing the dimensionality and storage requirements of data. Secondly, through deep learning technology, we design a point cloud thinning model, which can further reduce the data density while retaining key information. This deep learning model has strong generalization ability and can adapt to the needs of different point cloud data sets and application scenarios. The experimental results show that the proposed model can reduce the data volume and keep the information integrity of the data better. This is of great significance for some applications that need efficient data management, such as 3D target detection and recognition.

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Key words

Deep learning,PCA,point cloud,point cloud data thinning

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【要点】：本文提出了一种基于主成分分析（PCA）和深度学习技术的点云数据高效瘦化模型，旨在降低数据维度及存储需求，同时保留关键信息，提升数据处理效率。

【方法】：采用PCA进行数据降维以减少点云数据冗余，并结合深度学习设计瘦化模型，以在降低数据密度的同时保留关键信息。

【实验】：通过实验验证了所提模型在不同点云数据集上的瘦化效果，实验使用了多个标准点云数据集，结果表明模型能有效减小数据量并保持数据完整性。