Point Cloud Learning

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13 papers in the last four weeks, up 225% on the four weeks before. 0.1% of all new papers.

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Latest papers 97

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PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction
May 20, 2026Shizhe Chen, Paul Pacaud, Cordelia SchmidLanguage-Conditioned Robot Manipulation Point Cloud Learning
Inria, ´Ecole normale sup´erieure, CNRS, PSL Research University
Learning Structural Latent Points for Efficient Visual Representations in Robotic Manipulation
May 20, 2026Yicheng Jiang, Jiaxu Wang, Junhao He +8Point Cloud Learning Robotic Manipulation
1The Hong Kong University of Science and Technology · 3MMLab, The Chinese University of Hong Kong · 2The Hong Kong University of Science and Technology (Guangzhou) +4
Point Cloud Sequence Encoding for Material-conditioned Graph Network Simulators
May 20, 2026Philipp Dahlinger, Balázs Gyenes, Niklas Freymuth +6Sim-to-Real Transfer Point Cloud Learning
1Autonomous Learning Robots · 2Methods for Big Data · Institute of Vehicle System Technology
Bézier Degradation Modeling for LiDAR-based Human Motion Capture
May 19, 2026Xiaoqi An, Lin Zhao, Jun Li +2Point Cloud Learning 3D Human Pose Estimation
PCA Lab, School of Computer Science and Engineering, Nanjing University of Science and Technology · PCA Lab, School of Intelligence Science and Technology, Nanjing University
UST-Hand: An Uncertainty-aware Spatiotemporal Point Cloud Interaction Network for 3D Self-supervised Hand Pose Estimation
May 18, 2026Tianhao Han, Haoyang Zhang, Liang Xie +5Uncertainty Quantification Self-Supervised Learning
School of Computer Science, Shanghai Jiao Tong University · Defense Innovation Institute, Academy of Military Sciences · Tianjin Artificial Intelligence Innovation Center +3
Rethinking Point Clouds as Sequences: A Causal Next-Token Predictive Learning Framework
May 17, 2026Yumeng Yao, Jingzhi Dong, Haowen Gu +43D ViTs Self-Supervised Pre-Training
Nanjing University of Science and Technology · Hangzhou City University · East China Normal University +1
OPTNet: Ordering Point Transformer Network for Post-disaster 3D Semantic Segmentation
May 16, 2026Nhut Le, Ehsan Karimi, Maryam RahnemoonfarDisaster Damage Assessment Transformer
Computer Science and Engineering, Lehigh University, Bethlehem PA 18015, US · Civil and Environmental Engineering, Lehigh University, Bethlehem PA 18015, US
A Survey on Deep Learning Architectures for Point Cloud Classification and Segmentation
May 16, 2026Minhas Kamal, Hiranya Garbha Kumar, Balakrishnan Prabhakaran3D Point Cloud Segmentation 3D Part Segmentation
State University of New York at Albany, USA
Coarse Semantic Injection for LLM-Conditioned Structured Indoor Prediction
May 16, 2026Shuliang Zhu, Tomiwa Adey, Jinjia ZhouPoint Cloud Learning 3D Object Detection
Invaria: Learning Scale and Density Invariance in Point Clouds via Next-Resolution Prediction
May 15, 2026Chun-Peng Chang, Shaoxiang Wang, Alain Pagani +2Point Cloud Learning 3D Semantic Segmentation
TU Delft · DFKI
Neural Point-Forms
May 15, 2026Bruno Trentini, Jacob Hume, Vincenzo Antonio Isoldi +3Point Cloud Learning Geometric Representation Learning
University of Oxford Oxford, UK · School of Engineering and Applied Sciences Harvard University Cambridge, MA, US · Max Planck Institute of Molecular Cell Biology and Genetics Dresden, Germany
Learned Suppression for 3D Keypoint Detection with a Graph-Transformer Backbone
May 14, 2026Batuhan Arda Bekar, Can Sarı, Hüseyin Can Gülkan +13D ViTs Graph Neural Networks
Bahcesehir University Computer Engineering
DAP: Doppler-aware Point Network for Heterogeneous mmWave Action Recognition
May 10, 2026Jiaying Lin, Shiman Wu, Jinfu Liu +2Human Activity Recognition Point Cloud Learning
State Key Laboratory of General Artificial Intelligence, Peking University, Shenzhen Graduate School, Shenzhen, China · Huazhong University of Science and Technology, Wuhan, China · DJI Technology Co., Ltd., Shenzhen, China +2
Extrusion Segmentation Strategy to improve CAD Reconstruction from Point Cloud
May 9, 2026Said Harb, Mehdi Maboudi, Markus Gerke3D Reconstruction Point Cloud Learning
Institute of Geodesy and Photogrammetry, Technische Universität Braunschweig, Germany
The Wristband Gaussian Loss: Deterministic, Composable Latents via a Sphere-Interval Decomposition
May 9, 2026Mikhail Parakhin, André M. Carvalho, Patrick HaluptzokPoint Cloud Learning
Shopify
A meshfree exterior calculus for generalizable and data-efficient learning of physics from point clouds
May 8, 2026Benjamin D. Shaffer, Brooks Kinch, M. Ani Hsieh +1Point Cloud Learning PDE Surrogate Modeling
University of Pennsylvania Philadelphia, PA 19104 · *Mechanical Engineering and Applied Mechanics
Velox: Learning Representations of 4D Geometry and Appearance
May 6, 2026Anagh Malik, Dorian Chan, Xiaoming Zhao +3Representation Learning Point Cloud Learning
Apple · University of Toronto
Diffusion Masked Pretraining for Dynamic Point Cloud
May 5, 2026Zhuoyue Zhang, Jihua Zhu, Chaowei Fang +2Self-Supervised Pre-Training Point Cloud Learning
Xi’an Jiaotong University, Xi’an, China · School of Artificial Intelligence and Robotics, Hunan University, China · University of Western Australia, Perth, Australia
Mantis: Mamba-native Tuning is Efficient for 3D Point Cloud Foundation Models
May 5, 2026Zihao Guo, Yiding Sun, Jihua Zhu +23D Foundation Models Mamba
Xi’an Jiaotong University, Xi’an, China · School of Artificial Intelligence and Robotics, Hunan University, China · University of Western Australia, Perth, Australia
Learning Discriminative Signed Distance Functions from Multi-scale Level-of-detail Features for 3D Anomaly Detection
May 5, 2026Haibo Xiao, Hanzhe Liang, Jie Zhou +2Point Cloud Anomaly Detection Signed Distance Functions
College of Computer Science and Software Engineering, Shenzhen University · Biological and Life Sciences Division, Mohamed bin Zayed University of Artificial Intelligence, UAE · Shenzhen Audencia Financial Technology Institute, Shenzhen University, China +1
Super-Resolution of Airborne Laser Scanning Point Clouds for Forest Inventory
May 4, 2026Jinyuan Shao, Sangyoong Park, Chunxi Zhao +2Point Cloud Learning Airborne Laser Scanning
Department of Forestry and Natural Resources, Purdue University, USA · Lyles School of Civil and Construction Engineering, Purdue University, USA
PointCSP: Cross-Sample Semantic Propagation and Stability Preservation in Self-Supervised Point Cloud Learning
May 3, 2026Xinxing Yu, Ajian Liu, Sunyuan Qiang +5Self-Supervised Pre-Training Point Cloud Learning
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
BIMStruct3D: A Fully Automated Hybrid Learning Scan-to-BIM Pipeline with Integrated Topology Refinement
Apr 27, 2026Mahdi Chamseddine, Fabian Kaufmann, Marius Schellen +33D Reconstruction Point Cloud Learning
German Research Center for Artificial Intelligence (DFKI), Kaiserslautern, Germany · RPTU Kaiserslautern-Landau, Kaiserslautern, Germany
One Shot Learning for Edge Detection on Point Clouds
Apr 24, 2026Zhikun Tu, Yuhe Zhang, Yiou Jia +2Point Cloud Learning Few-Shot Learning
School of Information Science and Technology, Northwest University, Xi’an, China · Department of Computer Science, Tel Aviv University, Tel Aviv, Israel
FILTR: Extracting Topological Features from Pretrained 3D Models
Apr 24, 2026Louis Martinez, Maks OvsjanikovPoint Cloud Learning 3D Representation Learning
LIX, ´Ecole Polytechnique, IP Paris
EdgeFormer: local patch-based edge detection transformer on point clouds
Apr 23, 2026Yifei Xie, Zhikun Tu, Tong Yang +23D ViTs Point Cloud Learning
School of Information Science and Technology, Northwest University, 2026 Xuefu Street, Xi’an, 710127, Shannxi, China.
FlashFPS: Efficient Farthest Point Sampling for Large-Scale Point Clouds via Pruning and Caching
Apr 20, 2026Yuzhe Fu, Hancheng Ye, Cong Guo +7Efficient Inference GPU Acceleration
Helen · Duke University Durham, North Carolina, USA
UGD: An Unsupervised Geometric Distance for Evaluating Real-world Noisy Point Cloud Denoising
Apr 18, 2026Zhiyong Su, Jincan Wu, Yonghui Liu +2Point Cloud Learning
School of Automation, Nanjing University of Science and Technology, Nanjing 210094, China · School of Computer Science and Engineering, Nanjing University of Science and Technology, Nanjing 210094, China
TSM-Pose: Topology-Aware Learning with Semantic Mamba for Category-Level Object Pose Estimation
Apr 18, 2026Jinshuo Liu, Bingtao Ma, Junlin Su +6Point Cloud Learning Object Pose Estimation
Hangzhou Dianzi University · Shanghai Artificial Intelligence Laboratory
LOD-Net: Locality-Aware 3D Object Detection Using Multi-Scale Transformer Network
Apr 17, 2026Mustaqeem Khan, Aidana Nurakhmetova, Wail Gueaieb +13D ViTs Point Cloud Learning
College of Information Technology-United Arab Emirates University (UAEU), Al Ain, UAE · Mohamed bin Zayed University of Artificial Intelligence, Abu Dhabi, UAE · School of Elec. Eng. & Comp. Sc., University of Ottawa, Ottawa, ON, Canada

May 20, 2026, cs.ROJ/K move · Enter open · S save

PointACT: Vision-Language-Action Models with Multi-Scale Point-Action Interaction

Shizhe Chen, Paul Pacaud, Cordelia Schmid

Inria, ´Ecole normale sup´erieure, CNRS, PSL Research University

Language-Conditioned Robot Manipulation Point Cloud Learning Vision-Language-Action Models 3D Representation Learning

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Vision-Language-Action (VLA) models have shown strong potential for general-purpose robotic manipulation by leveraging large pretrained vision-language backbones. However, most existing VLAs rely primarily on 2D visual representations, which limit their ability to reason about fine-grained geometry and spatial grounding - capabilities that are essential for precise and robust manipulation in 3D environments. In this paper, we propose PointACT, a dual-system 3D-aware VLA policy that integrates hierarchical 3D point cloud representations directly into the action decoding process. PointACT employs a multi-scale point-action interaction mechanism with efficient bottleneck window self-attention, enabling evolving action tokens to densely attend to both local geometric detail and global scene structure. We evaluate PointACT on the LIBERO and RLBench benchmarks and systematically compare it against monolithic and dual-system VLA baselines, including variants augmented with point cloud inputs. PointACT achieves consistent improvements across both benchmarks, increasing success rates by 10% on the challenging RLBench-10Tasks suite over state-of-the-art pretrained VLAs, with even larger gains when the vision-language backbone is frozen and the action expert is trained from scratch. Extensive ablation studies demonstrate that tightly coupling hierarchical 3D geometry with pretrained 2D semantic representations is critical for robust and spatially grounded robot control. Our results also highlight the promise of pretrained 3D representations for 3D-aware VLA policies.

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