Besides, how to effectively integrate geometry-based and learning based techniques to develop 3D vision systems is still an open problem. It is therefore necessary to study general algorithms that can mitigate the domain gap between different types of 3D data. 3D data produced by different 3D imaging sensors (e.g., structured light, stereo, LiDAR, time-of-flight) have different characteristics. Although remarkable progress has been achieved in this area during the last few years, there are still several challenges that need to be addressed, such as the noisy, sparse, and irregular nature of point clouds, and the high cost to label 3D data. Driven by these factors, 3D vision has become an emerging and core component for numerous applications, such as autonomous driving, AR/VR, and robotics. Meanwhile, the increasing popularity of deep learning algorithms, such as convolutional neural networks and deep reinforcement learning, has further increased the usability of 3D vision systems. With the rapid development of 3D imaging sensors, such as depth cameras and laser scanning systems, 3D data has become increasingly accessible. IET Generation, Transmission & DistributionĬalls for Papers Deep Learning for 3D Vision.IET Electrical Systems in Transportation.IET Cyber-Physical Systems: Theory & Applications.IET Collaborative Intelligent Manufacturing.CAAI Transactions on Intelligence Technology.

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