IET CV: Deep Learning for 3D Vision Call for Papers

IET Computer Vision - Special Issue on "Deep Learning for 3D Vision"

Call for Papers

With the rapid development of 3D imaging sensors, such as depth
cameras and laser scanning systems, 3D data has become increasingly
accessible. 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. Driven by these factors, 3D vision has become an
emerging and core component for numerous applications, such as
autonomous driving, AR/VR, and robotics. 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. 3D data produced by different 3D imaging sensors (e.g.,
structured light, stereo, LiDAR, time-of-flight) have different
characteristics. It is therefore necessary to study general algorithms
that can mitigate the domain gap between different types of 3D
data. Besides, how to effectively integrate geometry-based and
learning based techniques to develop 3D vision systems is still an
open problem. The aim of this special issue of IET Computer Vision is
to collect and present the latest research development in
learning-based 3D vision theories and their applications, and to
inspire future research in this area. Papers working on 3D data
acquisition, 3D modelling, 3D data (including point cloud, voxels,
meshes) analysis, and their applications with deep learning are within
the scope of this special issue. Note that papers purely working on 2D
vision tasks (for example regular video processing) are out of the
scope of this special issue.

Guest Editors: Yulan Guo, Hanyun Wang, Stefano Berretti, Ronald Clark
and Mohammed Bennamoun.

Submissions must be made through ScholarOne by 31 December 2021. 

More information on this special issue and submitting an article can be found at