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

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.