Deep Learning for 3D Point Clouds: A Survey

Deep Learning for 3D Point Clouds: A Survey

23 Jun 2020 | Yulan Guo*, Hanyun Wang*, Qingyong Hu*, Hao Liu*, Li Liu, and Mohammed Bennamoun
This paper provides a comprehensive review of recent advancements in deep learning methods for 3D point clouds, focusing on three major tasks: 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It covers various datasets and evaluation metrics used in these tasks, and presents a taxonomy of existing deep learning methods. The paper highlights the unique challenges in processing 3D point clouds, such as small dataset sizes, high dimensionality, and unstructured nature. It discusses the contributions of different types of methods, including multi-view, volumetric, and point-based approaches, and provides detailed comparisons of their performance on several public datasets. The paper also offers insights and future research directions, emphasizing the need for more efficient and accurate methods to handle the complexities of 3D point cloud data.This paper provides a comprehensive review of recent advancements in deep learning methods for 3D point clouds, focusing on three major tasks: 3D shape classification, 3D object detection and tracking, and 3D point cloud segmentation. It covers various datasets and evaluation metrics used in these tasks, and presents a taxonomy of existing deep learning methods. The paper highlights the unique challenges in processing 3D point clouds, such as small dataset sizes, high dimensionality, and unstructured nature. It discusses the contributions of different types of methods, including multi-view, volumetric, and point-based approaches, and provides detailed comparisons of their performance on several public datasets. The paper also offers insights and future research directions, emphasizing the need for more efficient and accurate methods to handle the complexities of 3D point cloud data.
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