# Deep Learning for 3D Point Cloud Understanding: A Survey

arXiv: https://arxiv.org/abs/2009.08920 · HTML: https://mlsanity.com/paper/2009.08920/

## Key facts

- Cited by 11 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Sep 2020.

## Built on by (most cited first, 11 of 11)

- [3D Semantic Scene Completion: a Survey](https://mlsanity.com/paper/2103.07466/) (2021)
- [Comprehensive Review of Deep Learning-Based 3D Point Cloud Completion Processing and Analysis](https://mlsanity.com/paper/2203.03311/) (2022)
- [A Review of Panoptic Segmentation for Mobile Mapping Point Clouds](https://mlsanity.com/paper/2304.13980/) (2023)
- [Multi-to-Single Knowledge Distillation for Point Cloud Semantic Segmentation](https://mlsanity.com/paper/2304.14800/) (2023)
- [Revisiting Multi-modal 3D Semantic Segmentation in Real-world Autonomous Driving](https://mlsanity.com/paper/2310.08826/) (2023)
- [Robust 3D Point Clouds Classification based on Declarative Defenders](https://mlsanity.com/paper/2410.09691/) (2024)
- [Contrastive Embedding Distribution Refinement and Entropy-Aware Attention for 3D Point Cloud Classification](https://mlsanity.com/paper/2201.11388/) (2022)
- [Toward Generalist Neural Motion Planners for Robotic Manipulators: Challenges and Opportunities](https://mlsanity.com/paper/2603.24318/) (2026)
- [Open-Vocabulary Semantic Segmentation with Uncertainty Alignment for Robotic Scene Understanding in Indoor Building Environments](https://mlsanity.com/paper/2503.23105/) (2025)
- [3D-ANC: Adaptive Neural Collapse for Robust 3D Point Cloud Recognition](https://mlsanity.com/paper/2511.07040/) (2025)
- [Uncertainty-aware Prototype Learning with Variational Inference for Few-shot Point Cloud Segmentation](https://mlsanity.com/paper/2603.19757/) (2026)

## Abstract (arXiv)

The development of practical applications, such as autonomous driving and robotics, has brought increasing attention to 3D point cloud understanding. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unstructured and noisy 3D points. To demonstrate the latest progress of deep learning for 3D point cloud understanding, this paper summarizes recent remarkable research contributions in this area from several different directions (classification, segmentation, detection, tracking, flow estimation, registration, augmentation and completion), together with commonly used datasets, metrics and state-of-the-art performances. More information regarding this survey can be found at: https://github.com/SHI-Labs/3D-Point-Cloud-Learning.