Deep Learning-based 3D Point Cloud Classification: A Systematic Survey
  and Outlook
Huang Zhang Changshuo Wang Shengwei Tian Baoli Lu Liping Zhang Xin Ning Xiao Bai
In recent years, point cloud representation has become one of the research
hotspots in the field of computer vision, and has been widely used in many
fields, such as autonomous driving, virtual reality, robotics, etc. Although
deep learning techniques have achieved great success in processing regular
structured 2D grid image data, there are still great challenges in processing
irregular, unstructured point cloud data. Point cloud classification is the
basis of point cloud analysis, and many deep learning-based methods have been
widely used in this task. Therefore, the purpose of this paper is to provide
researchers in this field with the latest research progress and future trends.
First, we introduce point cloud acquisition, characteristics, and challenges.
Second, we review 3D data representations, storage formats, and commonly used
datasets for point cloud classification. We then summarize deep learning-based
methods for point cloud classification and complement recent research work.
Next, we compare and analyze the performance of the main methods. Finally, we
discuss some challenges and future directions for point cloud classification.