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

arXiv: https://arxiv.org/abs/1907.04444 · HTML: https://mlsanity.com/paper/1907.04444/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Jul 2019.

## Built on by (most cited first, 18 of 18)

- [The Hessigheim 3D (H3D) Benchmark on Semantic Segmentation of High-Resolution 3D Point Clouds and Textured Meshes from UAV LiDAR and Multi-View-Stereo](https://mlsanity.com/paper/2102.05346/) (2021)
- [Imbalanced Big Data Oversampling: Taxonomy, Algorithms, Software, Guidelines and Future Directions](https://mlsanity.com/paper/2107.11508/) (2021)
- [3D Vision with Transformers: A Survey](https://mlsanity.com/paper/2208.04309/) (2022)
- [A Survey on Mixup Augmentations and Beyond](https://mlsanity.com/paper/2409.05202/) (2024)
- [CAB: Comprehensive Attention Benchmarking on Long Sequence Modeling](https://mlsanity.com/paper/2210.07661/) (2022)
- [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://mlsanity.com/paper/2304.13980/) (2023)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [TUM-FAcCADE: Reviewing and enriching point cloud benchmarks for faccade segmentation](https://mlsanity.com/paper/2304.07140/) (2023)
- [A Review of Landcover Classification with Very-High Resolution Remotely Sensed Optical Images-Analysis Unit,Model Scalability and Transferability](https://mlsanity.com/paper/2202.03342/) (2022)
- [Linear Attention via Orthogonal Memory](https://mlsanity.com/paper/2312.11135/) (2023)
- [UVStyle-Net: Unsupervised Few-shot Learning of 3D Style Similarity Measure for B-Reps](https://mlsanity.com/paper/2105.02961/) (2021)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [Synthesizing realistic sand assemblies with denoising diffusion in latent space](https://mlsanity.com/paper/2306.04411/) (2023)
- [Classifying point clouds at the facade-level using geometric features and deep learning networks](https://mlsanity.com/paper/2402.06506/) (2024)
- [Zero-shot detection of buildings in mobile LiDAR using Language Vision Model](https://mlsanity.com/paper/2404.09931/) (2024)
- [Juggling With Representations: On the Information Transfer Between Imagery, Point Clouds, and Meshes for Multi-Modal Semantics](https://mlsanity.com/paper/2103.07348/) (2021)
- [Multimodal Classification: Current Landscape, Taxonomy and Future Directions](https://mlsanity.com/paper/2109.09020/) (2021)
- [Towards Training-Free Underwater 3D Object Detection from Sonar Point Clouds: A Comparison of Traditional and Deep Learning Approaches](https://mlsanity.com/paper/2508.18293/) (2025)

## Abstract (arXiv)

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.