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Deep Learning Based Point Cloud Processing Techniques
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DOI：10.1109/ACCESS.2022.3226211.png)
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Abstract
En 中文
In this study, deep learning techniques and algorithms used in point cloud processing have been analysed. Methods, technical properties and algorithms developed for 3D Object Classification and Segmentation, 3D object detection and tracking and 3D scene flow of point cloud data have been also analysed. 3D point cloud sensing techniques have been grouped as Multi-view, Volumetric approach and raw point cloud processing and mathematical models of them have been analysed. In 3D Object Classification and Segmentation, algorithms are given by analysing it in different categories as Convolutional Neural Network (CNN) based, Graph based, Hierarchical Data Structure-Based Methods and Others. 3D object detection and tracking, Segmentation-based, Frustum-based, Discretization-based analysed as point based and other methods. In each section, deep learning algorithms are compared with respect to applicability to real-time processing, amount of points, and relevance to large-scale or small-scale areas. In the last section, comparisons of point cloud processing methods are made and their advantages and disadvantages are given in the form of a table.
Keywords:
Deep learning
point cloud processing
3D image processing
3D computer vision
3D object classification and segmentation
Journal
IF:
3.6
Papers:
9.8W
Citations:
29.4W
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