Survey of 3-Dimensional Point Cloud Processing Based on Deep Learning

Deep learning has shown its superior performance in the structured data analysis such as 2-dimensional images. In recent years, with the development of LIDAR sensing equipment and related technologies, 3-dimensional point cloud scanning and acquisition has become more convenient. That makes the analysis and processing of unstructured point cloud data potential become an important research direction and obtain some progress in many fields such as computer graphics, robot, autonomous driving, virtual and augmented reality. A survey on the research of 3-dimensional point cloud processing of recent years is presented. Focusing on the application of deep learning in 3-dimensional point cloud shape analysis, structure extraction, detection and repair, we introduce the extraction method of point cloud topological structure, and compare the progress of the following research directions with the construction of neural networks as the main method: shape deformation, reconstruction, segmentation, classification, object tracking, scene flow estimation, object detection and pose estimation. Finally, we summarize the commonly used 3-dimensional point cloud public datasets, analyze and compare the characteristics and evaluation indicators of various point cloud processing task methods, and point out their advantages and disadvantages. The challenges and development directions of processing point cloud data based on deep learning are discussed.