Deep Learning Techniques for 3D Vision: From Reconstruction to Recognition

Dear Colleagues,

This Special Issue aims to compile cutting-edge research on deep learning methodologies applied to 3D vision tasks, spanning from 3D reconstruction to object recognition. As deep learning continues to revolutionize computer vision, extending these techniques to three-dimensional data presents both significant challenges and opportunities. The scope of this Special Issue includes novel neural network architectures for processing 3D data; advancements in 3D reconstruction from single or multiple views; semantic segmentation in 3D spaces; and object detection and recognition within point clouds or volumetric data.

By exploring these diverse yet interconnected topics, this Special Issue seeks to advance the understanding of how deep learning can be leveraged to solve complex problems in 3D vision, bridging the gap between theoretical research and practical applications.

We welcome contributions that address the efficient representation and manipulation of 3D data, the integration of multimodal inputs, real-time processing, and applications in areas such as robotics, autonomous driving, augmented reality, and medical imaging.

Technical Program Committee Member:

Mr. Matteo Basile, Sapienza University

Dr. Marco Raoul Marini
Dr. Goran Oreški
Prof. Luigi Cinque
Guest Editors

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