Point Cloud Generation

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2 papers in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.

Jul 13Week of Sep 28

Latest papers 16

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HuLiGen: Human LiDAR Generation from Parametric Body Models
Oct 7, 2026Salma Galaaoui, Nermin Samet, David PicardPoint Cloud Generation Conditional Generative Modeling
Valeo.ai, Paris, France · LIGM, CNRS, Univ Gustave Eiffel, ENPC, Institut Polytechnique de Paris, Marne-la-Vall´ee, France
EMERGE: Resolution-Agnostic Point Cloud Generation with Equivariant Graph-Based Diffusion
Sep 22, 2026Ilias Mitsouras, Nikolaos Chaidos, Giorgos Stamou +1Graph Neural Networks Equivariant Neural Networks
National Technical University of Athens Athens, Greece
MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillation
Sep 15, 2026Kamil Kwarciak, Marek WodzinskiFlow Matching One-Step Generative Modeling
Department of Measurement and Electronics, AGH University of Krakow, Krakow, Poland · Sano Centre for Computational Medicine, Krakow, Poland
GridFlow: Structured Latent Flow for Seamless City-Scale 3D Point Cloud Generation
Aug 30, 2026Xinyu Wang, Muhammad Ibrahim, Atif Mansoor +13D Scene Generation Point Cloud Generation
The University of Western Australia, Perth, WA 6009, Australia
Hierarchical Flow Matching for 3D Point Cloud Generation
Aug 6, 2026Linhao Wang, Qichang Zhang, Ye Su +1Flow Matching 3D Shape Generation
Shandong Normal University · University of Macau
Learning to Tessellate: Point Cloud Generation via Recursive Spectral Partitioning
Aug 3, 2026Monan Sun, Bangzhen Liu, Huaidong Zhang +1Point Cloud Learning 3D Shape Generation
South China University of Technology, China · University of Chinese Academy of Sciences, China · Singapore Management University, Singapore +1
FPSGen: Flexible Point Cloud Scene Generation with BEV-Supported Transport Flows
Jul 29, 2026Wenzhe He, Meng Wang, JiaWei Qian +3Point Cloud Completion 3D Scene Generation
College of Computer Science and Electronic Engineering, Hunan University, Hunan, China · College of Information Science and Engineering, Hunan Normal University, Hunan, China
Depth-Semantic Alignment and Affinity-Guided Fusion for Structured Radar Point Cloud Generation
Jun 25, 2026Amjad Hussain, Xin Qiu, Fuyuan Ai +4Camera-Radar Fusion Point Cloud Completion
Zhejiang University, China
3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning
Jun 6, 2026Jihun Cho, Soo-Yeon Jeong, Eun-Jeong Bae +13D Reconstruction Multi-View 3D Reconstruction
Dept. of Computer Engineering Pai Chai University Daejeon, Korea · Division of Software Engineering Pai Chai University Daejeon, Korea · Dept. of Dental Technology Bucheon University Bucheon, Korea
Extending Deep Event Visual Odometry with Sparse Point-Cloud Export
May 21, 2026Alireza Safdari, Sajad AshrafEvent-Based Vision Visual Odometry
TU Berlin
OmniLiDAR: A Unified Diffusion Framework for Multi-Domain 3D LiDAR Generation
May 13, 2026Youquan Liu, Weidong Yang, Ao Liang +9Synthetic Data Generation for Robotics 3D Scene Generation
College of Computer Science and Artificial Intelligence, Fudan University, Shanghai, China · School of Computing, Department of Computer Science, National University of Singapore, Singapore · College of Computer Science and Technology, Nanjing University of Aeronautics and Astronautics, Nanjing, China +5
Continuum Robot Modeling with Action Conditioned Flow Matching
May 9, 2026Jiong Lin, Jinchen Ruan, Hod Lipson3D Shape Generation Point Cloud Generation
Department of Mechanical Engineering, Columbia University, New York, NY, USA
Generalized Wasserstein Flow Matching: Transport Plans, Everywhere, All at Once
May 8, 2026Moritz Piening, Richard Duong, Gabriele SteidlFlow Matching Generative Modeling
Institut für Mathematik, Technische Universität Berlin, Germany
Point-MF: One-step Point Cloud Generation from a Single Image via Mean Flows
Apr 27, 2026Yuta Baba, Keiji YanaiFlow Matching One-Step Generative Modeling
The University of Electro-Communications 1-5-1 Chofugaoka, Chofu-shi, Tokyo 182-8585, Japan
AllShowers: One model for all calorimeter showers
Jan 16, 2026Thorsten Buss, Henry Day-Hall, Frank Gaede +2Neural Surrogate Modeling Generative Modeling
Institut für Experimentalphysik, Universität Hamburg, Hamburg, Germany · Deutsches Elektronen-Synchrotron DESY, Hamburg, Germany
4D-RaDiff: Latent Point Diffusion for 4D Radar Point Cloud Generation
Date pendingJimmie Kwok, Holger Caesar, Andras PalffySynthetic Data Augmentation Point Cloud Learning
Delft University of Technology, Netherlands · Perciv AI, Netherlands

Oct 7, 2026, cs.CVJ/K move · Enter open · S save

HuLiGen: Human LiDAR Generation from Parametric Body Models

Salma Galaaoui, Nermin Samet, David Picard

Valeo.ai, Paris, France · LIGM, CNRS, Univ Gustave Eiffel, ENPC, Institut Polytechnique de Paris, Marne-la-Vall´ee, France

Point Cloud Generation Conditional Generative Modeling 3D Human Pose Estimation

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LiDAR point clouds of humans are extremely expensive to collect and annotate, thus represent a scarce resource that hinders the development of human analysis using this modality. To alleviate this scarcity, prior work relies on simulated human LiDAR, but such samples do not fully reflect the geometry and sensing characteristics of real observations. In contrast, we introduce HuLiGen, a generative model that generates human LiDAR point clouds from a parametric body model, using a point transformer trained with a flow-matching objective. We show that our generated point clouds are closer to the real capture distribution. Using HuLiGen to generate synthetic data, we propose a synthetic-only pretraining scheme for LiDAR-based HPE that achieves state-of-the-art performance, with even larger gains in low-annotation and low-data regimes, where MPJPE is reduced by up to 50%. Code, models and generated samples are available at https://github.com/valeoai/HuLiGen.

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