# A comprehensive overview of deep learning techniques for 3D point cloud classification and semantic segmentation arXiv: https://arxiv.org/abs/2405.11903 ยท HTML: https://mlsanity.com/paper/2405.11903/ ## Key facts - Cited by 14 papers in the map (data through 14 Oct 2026). - Builds on 30 of its 52 references that are in the map. - First posted on arXiv 20 May 2024. ## Builds on (most cited first, 30 of 30) - [Dynamic Graph CNN for Learning on Point Clouds](https://mlsanity.com/paper/1801.07829/) (2018) - [PCT: Point cloud transformer](https://mlsanity.com/paper/2012.09688/) (2020) - [Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion](https://mlsanity.com/paper/2012.03762/) (2020) - [Point Convolutional Neural Networks by Extension Operators](https://mlsanity.com/paper/1803.10091/) (2018) - [Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network](https://mlsanity.com/paper/1811.02565/) (2018) - [MeshNet: Mesh Neural Network for 3D Shape Representation](https://mlsanity.com/paper/1811.11424/) (2018) - [Transfer Learning from Synthetic to Real LiDAR Point Cloud for Semantic Segmentation](https://mlsanity.com/paper/2107.05399/) (2021) - [LidarMultiNet: Towards a Unified Multi-Task Network for LiDAR Perception](https://mlsanity.com/paper/2209.09385/) (2022) - [Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud](https://mlsanity.com/paper/2012.10921/) (2020) - [RGCNN: Regularized Graph CNN for Point Cloud Segmentation](https://mlsanity.com/paper/1806.02952/) (2018) - [Paris-Lille-3D: a large and high-quality ground truth urban point cloud dataset for automatic segmentation and classification](https://mlsanity.com/paper/1712.00032/) (2017) - [Monte Carlo Convolution for Learning on Non-Uniformly Sampled Point Clouds](https://mlsanity.com/paper/1806.01759/) (2018) - [ConvPoint: Continuous Convolutions for Point Cloud Processing](https://mlsanity.com/paper/1904.02375/) (2019) - [PVT: Point-Voxel Transformer for Point Cloud Learning](https://mlsanity.com/paper/2108.06076/) (2021) - [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) - [Rotational Projection Statistics for 3D Local Surface Description and Object Recognition](https://mlsanity.com/paper/1304.3192/) (2013) - [PVNet: A Joint Convolutional Network of Point Cloud and Multi-View for 3D Shape Recognition](https://mlsanity.com/paper/1808.07659/) (2018) - [MeshWalker: Deep Mesh Understanding by Random Walks](https://mlsanity.com/paper/2006.05353/) (2020) - [SRINet: Learning Strictly Rotation-Invariant Representations for Point Cloud Classification and Segmentation](https://mlsanity.com/paper/1911.02163/) (2019) - [JSNet: Joint Instance and Semantic Segmentation of 3D Point Clouds](https://mlsanity.com/paper/1912.09654/) (2019) - [Campus3D: A Photogrammetry Point Cloud Benchmark for Hierarchical Understanding of Outdoor Scene](https://mlsanity.com/paper/2008.04968/) (2020) - [Semantic Segmentation on Swiss3DCities: A Benchmark Study on Aerial Photogrammetric 3D Pointcloud Dataset](https://mlsanity.com/paper/2012.12996/) (2020) - [FPS-Net: A Convolutional Fusion Network for Large-Scale LiDAR Point Cloud Segmentation](https://mlsanity.com/paper/2103.00738/) (2021) - [DeltaConv: Anisotropic Operators for Geometric Deep Learning on Point Clouds](https://mlsanity.com/paper/2111.08799/) (2021) - [PVRNet: Point-View Relation Neural Network for 3D Shape Recognition](https://mlsanity.com/paper/1812.00333/) (2018) - [Point Cloud Processing via Recurrent Set Encoding](https://mlsanity.com/paper/1911.10729/) (2019) - [PointSCNet: Point Cloud Structure and Correlation Learning Based on Space Filling Curve-Guided Sampling](https://mlsanity.com/paper/2202.10251/) (2022) - [PIG-Net: Inception based Deep Learning Architecture for 3D Point Cloud Segmentation](https://mlsanity.com/paper/2101.11987/) (2021) - [ODFNet: Using orientation distribution functions to characterize 3D point clouds](https://mlsanity.com/paper/2012.04708/) (2020) - [PointCaps: Raw Point Cloud Processing using Capsule Networks with Euclidean Distance Routing](https://mlsanity.com/paper/2112.11258/) (2021) ## Built on by (most cited first, 14 of 14) - [PartInstruct: Part-level Instruction Following for Fine-grained Robot Manipulation](https://mlsanity.com/paper/2505.21652/) (2025) - [CrossJEPA: Cross-Modal Joint-Embedding Predictive Architecture for Efficient 3D Representation Learning from 2D Images](https://mlsanity.com/paper/2511.18424/) (2025) - [A Deep Learning Approach to Identify Rock Bolts in Complex 3D Point Clouds of Underground Mines Captured Using Mobile Laser Scanners](https://mlsanity.com/paper/2506.20464/) (2025) - [Imitating Radiological Scrolling: A Global-Local Attention Model for 3D Chest CT Volumes Multi-Label Anomaly Classification](https://mlsanity.com/paper/2503.20652/) (2025) - [TACO-Net: Topological Signatures Triumph in 3D Object Classification](https://mlsanity.com/paper/2509.24802/) (2025) - [PointGS: Semantic-Consistent Unsupervised 3D Point Cloud Segmentation with 3D Gaussian Splatting](https://mlsanity.com/paper/2605.11520/) (2026) - [SemanticBridge - A Dataset for 3D Semantic Segmentation of Bridges and Domain Gap Analysis](https://mlsanity.com/paper/2512.15369/) (2025) - [HLS4PC: A Parametrizable Framework For Accelerating Point-Based 3D Point Cloud Models on FPGA](https://mlsanity.com/paper/2512.22139/) (2025) - [Overview and Comparison of AVS Point Cloud Compression Standard](https://mlsanity.com/paper/2602.08613/) (2026) - [Graph-Guided Dual-Level Augmentation for 3D Scene Segmentation](https://mlsanity.com/paper/2507.22668/) (2025) - [Layered Quantum Architecture Search for 3D Point Cloud Classification](https://mlsanity.com/paper/2603.20024/) (2026) - [Data-Efficient Semantic Segmentation of 3D Point Clouds via Open-Vocabulary Image Segmentation-based Pseudo-Labeling](https://mlsanity.com/paper/2604.11007/) (2026) - [A Unified Non-Parametric and Interpretable Point Cloud Analysis via t-FCW Graph Representation](https://mlsanity.com/paper/2605.15475/) (2026) - [Bayesian methods and Markov chain Monte Carlo algorithms for curve reconstruction and point cloud data analysis](https://mlsanity.com/paper/2608.26490/) (2026) ## Abstract (arXiv) Point cloud analysis has a wide range of applications in many areas such as computer vision, robotic manipulation, and autonomous driving. While deep learning has achieved remarkable success on image-based tasks, there are many unique challenges faced by deep neural networks in processing massive, unordered, irregular and noisy 3D points. To stimulate future research, this paper analyzes recent progress in deep learning methods employed for point cloud processing and presents challenges and potential directions to advance this field. It serves as a comprehensive review on two major tasks in 3D point cloud processing-- namely, 3D shape classification and semantic segmentation.