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A Review on Deep Learning Techniques for 3D Sensed Data Classification

Department of Civil, Environmental and Geomatic Engineering, University College London, Gower Street, London WC1E 6BT, UK
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Remote Sens. 2019, 11(12), 1499; https://doi.org/10.3390/rs11121499
Received: 15 April 2019 / Revised: 19 June 2019 / Accepted: 20 June 2019 / Published: 25 June 2019
(This article belongs to the Special Issue Point Cloud Processing in Remote Sensing)
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. View Full-Text
Keywords: point cloud; deep learning; classification; semantics; segmentation; machine learning point cloud; deep learning; classification; semantics; segmentation; machine learning
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Griffiths, D.; Boehm, J. A Review on Deep Learning Techniques for 3D Sensed Data Classification. Remote Sens. 2019, 11, 1499.

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