# Review: deep learning on 3D point clouds

arXiv: https://arxiv.org/abs/2001.06280 · HTML: https://mlsanity.com/paper/2001.06280/

## Key facts

- Cited by 29 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jan 2020.

## Built on by (most cited first, 29 of 29)

- [A Survey on Deep Domain Adaptation for LiDAR Perception](https://mlsanity.com/paper/2106.02377/) (2021)
- [Paris-CARLA-3D: A Real and Synthetic Outdoor Point Cloud Dataset for Challenging Tasks in 3D Mapping](https://mlsanity.com/paper/2111.11348/) (2021)
- [HyperPocket: Generative Point Cloud Completion](https://mlsanity.com/paper/2102.05973/) (2021)
- [Advancing 3D Point Cloud Understanding through Deep Transfer Learning: A Comprehensive Survey](https://mlsanity.com/paper/2407.17877/) (2024)
- [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://mlsanity.com/paper/2304.13980/) (2023)
- [Leveraging Large-Scale Pretrained Vision Foundation Models for Label-Efficient 3D Point Cloud Segmentation](https://mlsanity.com/paper/2311.01989/) (2023)
- [Deep Learning on 3D Semantic Segmentation: A Detailed Review](https://mlsanity.com/paper/2411.02104/) (2024)
- [Literature Review: Computer Vision Applications in Transportation Logistics and Warehousing](https://mlsanity.com/paper/2304.06009/) (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)
- [Survey and Systematization of 3D Object Detection Models and Methods](https://mlsanity.com/paper/2201.09354/) (2022)
- [PointNorm: Dual Normalization is All You Need for Point Cloud Analysis](https://mlsanity.com/paper/2207.06324/) (2022)
- [Weakly Supervised 3D Instance Segmentation without Instance-level Annotations](https://mlsanity.com/paper/2308.01721/) (2023)
- [Deep Neural Watermarking for Robust Copyright Protection in 3D Point Clouds](https://mlsanity.com/paper/2510.27533/) (2025)
- [The Future of Intelligent Healthcare: A Systematic Analysis and Discussion on the Integration and Impact of Robots Using Large Language Models for Healthcare](https://mlsanity.com/paper/2411.03287/) (2024)
- [Angle Based Feature Learning in GNN for 3D Object Detection using Point Cloud](https://mlsanity.com/paper/2108.00780/) (2021)
- [Hypergraph Convolutional Network based Weakly Supervised Point Cloud Semantic Segmentation with Scene-Level Annotations](https://mlsanity.com/paper/2211.01174/) (2022)
- [Local region-learning modules for point cloud classification](https://mlsanity.com/paper/2303.17338/) (2023)
- [Deep Learning-Based 3D Instance and Semantic Segmentation: A Review](https://mlsanity.com/paper/2406.13308/) (2024)
- [A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/2605.17131/) (2026)
- [End-to-End Intelligent Framework for Rockfall Detection](https://mlsanity.com/paper/2102.06491/) (2021)
- [Deep Learning and Earth Observation to Support the Sustainable Development Goals](https://mlsanity.com/paper/2112.11367/) (2021)
- [Learning task-specific features for 3D pointcloud graph creation](https://mlsanity.com/paper/2209.00949/) (2022)
- [Effective Early Stopping of Point Cloud Neural Networks](https://mlsanity.com/paper/2209.15308/) (2022)
- [pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2307.14777/) (2023)
- [DG-PPU: Dynamical Graphs based Post-processing of Point Clouds extracted from Knee Ultrasounds](https://mlsanity.com/paper/2411.08926/) (2024)
- [Unlocking Hidden Potential in Point Cloud Networks with Attention-Guided Grouping-Feature Coordination](https://mlsanity.com/paper/2509.16639/) (2025)
- [Deep Imbalanced Multi-Target Regression: 3D Point Cloud Voxel Content Estimation in Simulated Forests](https://mlsanity.com/paper/2511.12740/) (2025)
- [Task-Oriented Wireless Transmission of 3D Point Clouds: Geometric Versus Semantic Robustness](https://mlsanity.com/paper/2603.13560/) (2026)
- [Spatially-Aware Evaluation Framework for Aerial LiDAR Point Cloud Semantic Segmentation: Distance-Based Metrics on Challenging Regions](https://mlsanity.com/paper/2603.22420/) (2026)

## Abstract (arXiv)

Point cloud is point sets defined in 3D metric space. Point cloud has become one of the most significant data format for 3D representation. Its gaining increased popularity as a result of increased availability of acquisition devices, such as LiDAR, as well as increased application in areas such as robotics, autonomous driving, augmented and virtual reality. Deep learning is now the most powerful tool for data processing in computer vision, becoming the most preferred technique for tasks such as classification, segmentation, and detection. While deep learning techniques are mainly applied to data with a structured grid, point cloud, on the other hand, is unstructured. The unstructuredness of point clouds makes use of deep learning for its processing directly very challenging. Earlier approaches overcome this challenge by preprocessing the point cloud into a structured grid format at the cost of increased computational cost or lost of depth information. Recently, however, many state-of-the-arts deep learning techniques that directly operate on point cloud are being developed. This paper contains a survey of the recent state-of-the-art deep learning techniques that mainly focused on point cloud data. We first briefly discussed the major challenges faced when using deep learning directly on point cloud, we also briefly discussed earlier approaches which overcome the challenges by preprocessing the point cloud into a structured grid. We then give the review of the various state-of-the-art deep learning approaches that directly process point cloud in its unstructured form. We introduced the popular 3D point cloud benchmark datasets. And we also further discussed the application of deep learning in popular 3D vision tasks including classification, segmentation and detection.