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3D data is a valuable asset the computer vision filed as it provides richinformation about the full geometry of sensed objects and scenes. Recently,with the availability of both large 3D datasets and computational power, it istoday possible to consider applying deep learning to learn specific tasks on 3Ddata such as segmentation, recognition and correspondence. Depending on theconsidered 3D data representation, different challenges may be foreseen inusing existent deep learning architectures. In this work, we provide acomprehensive overview about various 3D data representations highlighting thedifference between Euclidean and non-Euclidean ones. We also discuss how DeepLearning methods are applied on each representation, analyzing the challengesto overcome.

A survey on Deep Learning Advances on Different 3D Data Representations