3D Deep Learning is gaining more importance nowadays with vital application needs in self-driving vehicles, autonomous robots, augmented reality and virtual reality, 3D graphics, and 3D games. Unlike 2D data, 3D data is complex with more parameters and features. Collecting 3D data and transforming it from one representation to another is a tedious process. Thus 3D deep learning is more time consuming and error-prone than 2D Computer Vision. Though there are nicely-performing models, datasets, metrics, graphics tools, and visualization tools published in recent years, integrating different approaches is quite a non-trivial job for researchers and practitioners.
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