Convolutional Neural Networks for 3D Vision System Data : A review

3D vision systems are becoming increasingly accessible and as such there has been a lot of progress in the design of 3D Convolutional Neural Networks (3D CNNs). This paper will provide a review of the significant enhancements which have been made in deep learning techniques for understanding data fr...

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Bibliographic Details
Main Authors: OrMahony, Niall, Campbell, Sean, Krpalkova, Lenka, Carvalho, Anderson, Velasco-Hernandez, Gustavo Adolfo, Riordan, Daniel, Walsh, Joseph
Format: Conference Proceeding
Language:English
Subjects:
3D Vision Systems
3DCNNs
Computer architecture
Deep Learning
Image segmentation
Kernel
Shape
Task analysis
Three-dimensional displays
Two dimensional displays
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Summary:3D vision systems are becoming increasingly accessible and as such there has been a lot of progress in the design of 3D Convolutional Neural Networks (3D CNNs). This paper will provide a review of the significant enhancements which have been made in deep learning techniques for understanding data from 3D vision systems in the tasks of 3D object classification and 3D semantic segmentation. We compare the documented results the state-of-the-art architectures have achieved on benchmark datasets and outline the advantages of the different techniques which have been developed in a number of different categories of approaches including view-based, voxelbased and point-based architectures amongst others. We also give insights into common themes and trends in this very active field of research.
ISSN:2156-8073
DOI:10.1109/ICSensT.2018.8603642