A review on deep learning techniques for 3D sensed data classification

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.

25 pages, 9 figures. Review paper

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• PointNet++: Deep Hierarchical Feature Learning on Point Sets in a Metric Space
• Learning to Segment Object Candidates
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Cited by in corpus (5)

• Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions
• Segmentation of structural parts of rosebush plants with 3D point-based deep learning methods
• Deep Learning on 3D Semantic Segmentation: A Detailed Review
• Juggling With Representations: On the Information Transfer Between Imagery, Point Clouds, and Meshes for Multi-Modal Semantics
• Simple Kinesthetic Haptics for Object Recognition