3D Point Cloud Segmentation

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3 papers in the last four weeks, against 2 the four weeks before. 0.0% of all new papers.

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FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud
May 9, 2026Yu Li, Volker Schwieger3D Point Cloud Segmentation Autonomous Driving Perception
Institute of Engineering Geodesy, University of Stuttgart, Stuttgart, Germany
ClickSeg3D: Few-Click Interactive Segmentation via Semantic Embeddings
May 9, 2026Xueyang Kang, Zijian Yu, Kourosh Khoshelham +13D Point Cloud Segmentation 3D Instance Segmentation
School of Electrical and Electronic Engineering, Nanyang Technological University · University of Science and Technology of China · Faculty of Architecture and the Built Environment, Delft University of Technology
Channel-Level Relation to Attentive Aggregation with Neighborhood-Homogeneity Constraint for Point Cloud Analysis
May 4, 2026Jiaqi Shi, Jin Xiao, Xiaoguang Hu +53D Point Cloud Segmentation Point Cloud Classification
School of Automation Science and Electrical Engineering, Beihang University, Beijing, China · Wuhan Leaddo Measuring & Control Technology, Wuhan, China · School of Artificial Intelligence, Beihang University, Beijing, China
VoxAfford: Multi-Scale Voxel-Token Fusion for Open-Vocabulary 3D Affordance Detection
May 2, 2026Haowen Sun, Shaolong Zhang, Mingyang Li +63D Point Cloud Segmentation 3D Scene Understanding
National Key Laboratory of Human-Machine Hybrid Augmented Intelligence, Institute of Artificial Intelligence and Robotics, Xi’an Jiaotong University, Xi’an, 710049
Learning to Identify Out-of-Distribution Objects for 3D LiDAR Anomaly Segmentation
Apr 26, 2026Simone Mosco, Daniel Fusaro, Alberto PrettoPoint Cloud Anomaly Detection 3D Point Cloud Segmentation
University of Padova, Italy
SparseGF: A Height-Aware Sparse Segmentation Framework with Context Compression for Robust Ground Filtering Across Urban to Natural Scenes
Apr 23, 2026Nannan Qin, Pengjie Tao, Haiyan Guan +43D Point Cloud Segmentation Spatial Generalization
School of Remote Sensing and Geomatics Engineering, Nanjing University of Information Science and Technology, Nanjing, 210044, JiangSu, China · Tiandu-Nuist Deep Space Exploration Laboratory, Nanjing, 210044, JiangSu, China · School of Remote Sensing and Information Engineering, Wuhan University, Wuhan, 430079, Hubei, China +4
TowerDataset: A Heterogeneous Benchmark for Transmission Corridor Segmentation with a Global-Local Fusion Framework
Apr 18, 2026Xu Cui, Xinyan Liu, Chen Yang +43D Point Cloud Segmentation Industrial Inspection
Harbin Institute of Technology (Weihai) · University of the Chinese Academy of Sciences · City University of Hong Kong
JOPP-3D: Joint Open Vocabulary Semantic Segmentation on Point Clouds and Panoramas
Mar 6, 2026Sandeep Inuganti, Hideaki Kanayama, Kanta Shimizu +43D Point Cloud Segmentation Open-Vocabulary 3D Segmentation
German Research Center for Artificial Intelligence, DFKI, Germany · RPTU Kaiserslautern, Germany · Ricoh Company, Ltd. Japan +1
Resolving Primitive-Sharing Ambiguity in Long-Tailed TLS-Based Industrial MEP Point Cloud Segmentation via Spatial Context Constraints
Jan 27, 2026Chao Yin, Qing Han, Zhiwei Hou +5Class-Imbalanced Learning 3D Point Cloud Segmentation
Guangzhou Institute of Geography, Guangdong Academy of Sciences, Guangzhou, China · School of Geography and Tourism, Hengyang Normal University, Hengyang, China · Southern Marine Science and Engineering Guangdong Laboratory (Guangzhou), Guangzhou, China +3
G2P: Gaussian-to-Point Attribute Alignment for Boundary-Aware 3D Segmentation
Jan 7, 2026Hojun Song, Chae-yeong Song, Jeong-hun Hong +53D Point Cloud Segmentation 3D Semantic Segmentation
Kyungpook National University · Korea Electronics Technology Institute · Adobe Research +1
Few to Big: Prototype Expansion Network via Diffusion Learner for Point Cloud Few-shot Semantic Segmentation
Sep 16, 2025Qianguang Zhao, Dongli Wang, Yan Zhou +23D Point Cloud Segmentation Prototype Learning
School of Mathematics and Computational Science, Xiangtan University, Xiangtan 411105, China · School of Automation and Electronics Information, Xiangtan University, Xiangtan 411105, China · School of Automation, Shanghai Jiao Tong University, Shanghai 200232, China
RoofSeg: An edge-aware transformer-based network for end-to-end roof plane segmentation
Aug 26, 2025Siyuan You, Guozheng Xu, Pengwei Zhou +33D Point Cloud Segmentation 3D Instance Segmentation
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China · School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China · Wuhan Unversity Shenzhen Research Institute, Shenzhen 518057, China.

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FugSeg: Fast Uncertainty-aware Ground Segmentation for 3D Point Cloud

Yu Li, Volker Schwieger

Institute of Engineering Geodesy, University of Stuttgart, Stuttgart, Germany

3D Point Cloud Segmentation Autonomous Driving Perception

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In LiDAR-based environment perception systems, ground segmentation is a key preprocessing step supporting various applications such as mapping and navigation. Although extensively studied, problems such as reflection noise and isolated ground remain challenging. To address these issues, we propose FugSeg, a fast uncertainty-aware ground segmentation method. A polar grid map is adopted as the point cloud representation to ensure generalizability across LiDAR types. Building on that, we develop a within- and cross-segment ground labeling strategy that identifies not only directly visible ground cells but also those that are isolated or occluded. During this process, an adaptive slope is introduced, which incorporates measurement uncertainties to enhance its reliability under complex terrain. Finally, to achieve point-level ground segmentation, a fine-grained ground elevation estimation method is introduced. Throughout the complete workflow, reflection noise is explicitly handled via the proposed noisy ground cells. We conduct comprehensive evaluations on four public datasets covering both structured and unstructured environments. Results show that FugSeg outperforms state-of-the-art non-learning methods, achieving the highest F1, accuracy, and mIoU across all datasets, while maintaining the fastest runtime (135 Hz and 487 Hz for 64- and 32-layer LiDARs) using a single CPU thread, making it suitable for resource-limited systems. The code will be available at https://github.com/Leo-YuLi/FugSeg.

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