  Over the past decade deep learning has driven progress in 2D image
understanding. Despite these advancements, techniques for automatic 3D sensed
data understanding, such as point clouds, is comparatively immature. However,
with a range of important applications from indoor robotics navigation to
national scale remote sensing there is a high demand for algorithms that can
learn to automatically understand and classify 3D sensed data. In this paper we
review the current state-of-the-art deep learning architectures for processing
unstructured Euclidean data. We begin by addressing the background concepts and
traditional methodologies. We review the current main approaches including;
RGB-D, multi-view, volumetric and fully end-to-end architecture designs.
Datasets for each category are documented and explained. Finally, we give a
detailed discussion about the future of deep learning for 3D sensed data, using
literature to justify the areas where future research would be most valuable.
