Griffiths, D;
Boehm, J;
(2019)
A Review on Deep Learning Techniques for 3D Sensed Data Classification.
Remote Sensing
, 11
(12)
, Article 1499. 10.3390/rs11121499.
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Abstract
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.
| Type: | Article |
|---|---|
| Title: | A Review on Deep Learning Techniques for 3D Sensed Data Classification |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.3390/rs11121499 |
| Publisher version: | https://doi.org/10.3390/rs11121499 |
| Language: | English |
| Additional information: | This is an open access article distributed under the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0). |
| Keywords: | point cloud; deep learning; classification; semantics; segmentation; machine learning |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Civil, Environ and Geomatic Eng |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10076807 |
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