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This chapter presents the principles of point cloud learning, including the foundations of deep learning and classical neural networks applied to point clouds. The first part covers the basic concepts of deep learning and provides a taxonomy of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), among others. The second part focuses on the design of common point cloud learning networks, such as the PointNet series, point cloud transformers, and an efficient algorithm called Point Voxel CNN.

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Learning Basics for 3D Point Clouds

  • 章节

  • 发表日期：

    2025年

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  • Wei Gao,
  • Ge Li

摘要

This chapter presents the principles of point cloud learning, including the foundations of deep learning and classical neural networks applied to point clouds. The first part covers the basic concepts of deep learning and provides a taxonomy of neural networks, including convolutional neural networks (CNNs), recurrent neural networks (RNNs), and graph neural networks (GNNs), among others. The second part focuses on the design of common point cloud learning networks, such as the PointNet series, point cloud transformers, and an efficient algorithm called Point Voxel CNN.