Self-Supervised Point Cloud Learning

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22 papers2 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

GEKey: Learning Geodesic Eccentric 3D Keypoints via Self-Supervision
Nov 5, 2026Pierre Onghena, Santiago Velasco-Forero, Beatriz MarcoteguiKeypoint Detection Self-Supervised Point Cloud Learning
Mines Paris – PSL University Centre for Statistics and Images (STIM)
FOSCo-Net: Ego-centric Voxel Completion for Forest LiDAR Occlusions
Nov 4, 2026Iman Esfandiyar, Dominik BelterPoint Cloud Completion Robot Traversability Estimation
Poznań University of Technology, Institute of Robotics and Machine Intelligence, Poznań, Poland · Łukasiewicz Research Network – Poznań Institute of Technology, Center of Digital Transformation, Department of Artificial Intelligence, Poznań, Poland
PT $^3$ : Prototype-Guided Self-Supervised Pretraining for Generalizable mmWave Human Point Cloud Representation
Oct 28, 2026Ziyang Liu, Kieren Yu, Xiao Yang +2Self-Supervised Point Cloud Learning mmWave Radar Sensing
The Hong Kong University of Science and Technology (Guangzhou)
SIEVE: Overcoming Topological Obstruction in Equivariant Self-Supervised Learning
Oct 15, 2026Jongann Lee, Sun Woo Park, Yun Young ChoiSelf-Supervised Point Cloud Learning Point Cloud Learning
Max Planck Institute of Mathematics Bonn, Germany · SolverX Seoul, Republic of Korea
Motion-Saliency Complementary Masked Modeling for Point Cloud Video Understanding
Aug 31, 2026Wei Wang, Yiding Sun, Yuyan Wang +4Masked Modeling Point Cloud Learning
School of Computer Science and Technology, Xi’an Jiaotong University, Xi’an, 710049, China · Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, 518107, China · School of Software Engineering, Xi’an Jiaotong University, Xi’an, 710049, China +4
Joint-Embedding Prediction of Masked Point Tubes for Self-Supervised Learning on 4D Point Cloud Videos
Aug 25, 2026Jheng-Ling Lee, Shang-Tse ChenSelf-Supervised Point Cloud Learning Self-Supervised Pre-Training
Department of Computer Science and Information Engineering, National Taiwan University
Emergent 3D Instance Segmentation from Self-Supervised Point Transformers
Aug 16, 2026Ted Lentsch, Santiago Montiel-Marín, Holger Caesar +1Self-Supervised Point Cloud Learning 3D Instance Segmentation
Department of Cognitive Robotics, Delft University of Technology, The Netherlands · Department of Electronics, University of Alcalá, Spain
GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure
Aug 14, 2026Mohamed Abdelsamad, Bin Yang, Michael Ulrich +4Self-Supervised Point Cloud Learning Lidar-Based 3D Object Detection
Bosch Center for AI · University of Freiburg · University of Luebeck
Vernata: Self-Supervised Learning of LiDAR Point Representations
Aug 7, 2026Oliver Lemke, Alexander Liniger, Abel Gawel +1Self-Supervised Point Cloud Learning Self-Supervised Learning
1Robotics and AI Institute · 2ETH Zurich
On the Efficacy of Self-Supervised Point Cloud Encoders for Efficient 3D Large Language Models
Jul 31, 2026Yao Zheng, Tian ZhangSelf-Supervised Pre-Training Point Cloud Learning
Beijing University of Posts and Telecommunications
Annotation-Free Furniture Codes: What They Encode, and How Far They Transfer
Jul 11, 2026Benjamin FriedmanSelf-Supervised Point Cloud Learning Finite Scalar Quantization
DLR Group
Self-Supervised Pretraining Improves Cross-Site and Cross-Scale Robustness of Point Cloud Leaf-Wood Segmentation
Jul 8, 2026Heeju Mun, Tackang Yang, Yunsoo Nam +1Self-Supervised Pre-Training Domain Generalization
Department of Landscape Architecture and Rural Systems Engineering, Seoul National University, Seoul, South Korea · Interdisciplinary Program in Landscape Architecture, Seoul National University, South Korea · Integrated Major in Smart City Global Convergence, Seoul National University, Seoul, Republic of Korea +1
Tri-Efficient Transfer Learning for Point Cloud Videos
Jun 23, 2026Yiding Sun, Dongxu Zhang, Jihua Zhu +6Point Cloud Understanding Self-Supervised Visual Representation Learning
School of Software Engineering, Xi’an Jiaotong University · School of Information and Communication Engineering, Xi’an Jiaotong University · SIGS, Tsinghua University
Efficient RWKV-based Representation Learning for 3D Point Clouds
Jun 9, 2026Yun Liu, Xuefeng Yan, Liangliang Nan +5Self-Supervised Point Cloud Learning Point Cloud Learning
School of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China · Shenzhen Institute of Research, Nanjing University of Aeronautics and Astronautics, Shenzhen, China · Collaborative Innovation Center of Novel Software Technology and Industrialization, Nanjing, China +2
Rethinking Point Clouds as Sequences: A Causal Next-Token Predictive Learning Framework
May 17, 2026Yumeng Yao, Jingzhi Dong, Haowen Gu +4Point Cloud Learning 3D ViTs
Nanjing University of Science and Technology · Hangzhou City University · East China Normal University +1
Diffusion Masked Pretraining for Dynamic Point Cloud
May 5, 2026Zhuoyue Zhang, Yiding Sun, Chaowei Fang +4Self-Supervised Pre-Training Point Cloud Learning
School of Software Engineering, Xi’an Jiaotong University, Xi’an, China · School of Artificial Intelligence and Robotics, Hunan University, Changsha, China · Department of Computer Science and Software Engineering, University of Western Australia, Perth, Australia
PointCSP: Cross-Sample Semantic Propagation and Stability Preservation in Self-Supervised Point Cloud Learning
May 3, 2026Xinxing Yu, Ajian Liu, Sunyuan Qiang +5Self-Supervised Point Cloud Learning Self-Supervised Pre-Training
Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China · MAIS, The Institute of Automation of the Chinese Academy of Sciences, Beijing, China · Southwest Institute of Technical Physics, Chengdu, China +1
LIDARLearn: A Unified Deep Learning Library for 3D Point Cloud Classification, Segmentation, and Self-Supervised Representation Learning
Apr 12, 2026Said Ohamouddou, Hanaa El Afia, Abdellatif El Afia +1Self-Supervised Point Cloud Learning Point Cloud Learning
ENSIAS, Mohammed V University, Rabat, Morocco
Towards Foundation Models for 3D Scene Understanding: Instance-Aware Self-Supervised Learning for Point Clouds
Mar 26, 2026Bin Yang, Mohamed Abdelsamad, Miao Zhang +1Self-Supervised Point Cloud Learning Point Cloud Learning
Bosch Center for AI, Stuttgart, Germany · Institute for Neuro- and Bioinformatics, University of Lübeck, Lübeck, Germany · Robert Learning Lab, University of Freiburg, Freiburg, Germany
Utonia: Toward One Encoder for All Point Clouds
Mar 3, 2026Yujia Zhang, Xiaoyang Wu, Yunhan Yang +63D Foundation Models 3D ViTs
The University of Hong Kong · Xiaomi · The Chinese University of Hong Kong
Foundation model for particle collision data analysis
Jan 23, 2026Ting-Hsiang Hsu, Bai-Hong Zhou, Qibin Liu +8High-Energy Physics Self-Supervised Point Cloud Learning
Department of Physics, National Taiwan University, Taipei, Taiwan · Tsung-Dao Lee Institute, Shanghai Jiao Tong University, Shanghai, China · Fundamental Physics Directorate, SLAC National Accelerator Laboratory, Menlo Park, USA +3
point2vec for Self-Supervised Representation Learning on Point Clouds
Mar 29, 2023Karim Knaebel, Jonas Schult, Alexander Hermans +1Self-Supervised Point Cloud Learning Point Cloud Learning
RWTH Aachen University, Germany

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GEKey: Learning Geodesic Eccentric 3D Keypoints via Self-Supervision

Pierre Onghena, Santiago Velasco-Forero, Beatriz Marcotegui

Mines Paris – PSL University Centre for Statistics and Images (STIM)

Keypoint Detection Self-Supervised Point Cloud Learning Geometric Representation Learning

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Point clouds represent 3D object geometry as sets of spatial coordinates. A fundamental task in their analysis is keypoint detection, i.e., identifying a sparse subset of points that captures the most salient features. While supervised methods rely on manual annotations, unsupervised approaches leverage intrinsic geometric properties to localize keypoints without human input. We introduce GEKey, a self-supervised framework that uses the geodesic eccentricity transform, an SE(3)-invariant geometric measure, to identify instance-specific salient points. These points serve as pseudo-labels to train a model that aligns its predictions with the object's intrinsic structure. GEKey is simple, efficient, and robust, preserving intra-class variation by balancing instance-specific and category-level structure. Extensive experiments on ShapeNetCoreV2, KeypointNet, CAPE, ITOP, and DeformingThings4D show state-of-the-art performance in both keypoint detection and alignment, with strong resilience to noise, downsampling, deformations, and real-world conditions. The code and pretrained models are available at https://github.com/Pierreoo/GEKey.