Geometric Representation Learning

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28 papers in the last four weeks, up 155% on the four weeks before. 0.3% of all new papers.

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Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks
Jul 17, 2026Sanjay ChakrabortyGraph Neural Networks Materials Property Prediction
Department of Computer and Information Science (IDA), REAL, AIICS, Linköping University, Linköping, Sweden
E3DGS: Unified Geometric-Photometric Equivariance for 3D Gaussian Splatting via Color-as-Geometry Embedding
Jul 17, 2026Chankyo Kim, Maani Ghaffari3D Gaussian Splatting Equivariant Neural Networks
University of Michigan, Ann Arbor, MI, USA
SUFLECA: Scaling Up Feature Learning for CAD-to-image Alignment
Jul 16, 2026Saad Ejaz, Miguel Fernandez-Cortizas, Javier Civera +2Object Pose Estimation Geometric Representation Learning
Automation and Robotics Research Group, Interdisciplinary Centre for Security, Reliability, and Trust (SnT), University of Luxembourg, Luxembourg · I3A, Universidad de Zaragoza
SeeSE3: Emergence of 3D Space in Vision Features
Jul 15, 2026Caroline Chen, Sayna Ebrahimi, Fedor Kitashov +4Geometric Representation Learning Visual Odometry
Google DeepMind
Contrastive-Collapsed Loss for Flexible and Geometrically Optimal Embeddings and Faster Convergence
Jul 14, 2026Blanca Cano-Camarero, Ángela Fernández-Pascual, José R. DorronsoroGeometric Representation Learning Neural Collapse
1Departamento de Ingeniería Informática, Universidad Autónoma de Madrid, Calle Francisco Tomás y Valiente, 11, 28049, Madrid, España
IG-GAN: A Generative Adversarial Network for Aerodynamic Data Generation Based on Intrinsic Geometry
Jul 13, 2026Ying Yan, Liwei Hu, Xiaoming ZhangSynthetic Data Generation Geometric Representation Learning
School of Information Science and Engineering, Hebei University of Science and Technology, Shijiazhuang, 050091, China
TriCons-Pose: Triangle-Invariant Geometric Consistency Learning for Category-Level Object Pose Estimation
Jul 12, 2026Zuzhi Yang, Shuai Wang, Mounir Kaaniche +4Object Pose Estimation Geometric Representation Learning
School of Cyberspace, Hangzhou Dianzi University, Hangzhou 310018, China · Universit´e Sorbonne Paris Nord, L2TI, UR 3043, F-93430, Villetaneuse, France · Universit´e Paris-Saclay, Centrale-Sup´elec, CVN, 91190 Gif-sur-Yvette, France +2
Learning to Unify Deformable Shape and Texture Representations for Cardiac Video Classification
Jul 8, 2026Tonmoy Hossain, Miaomiao ZhangGeometric Representation Learning Medical Image Classification
Department of Computer Science, University of Virginia, Virginia, USA · Department of Electrical and Computer Engineering, University of Virginia, USA
Riemannian Geometry for Pre-trained Language Model Embeddings
Jul 8, 2026Szczepan Konior, Alexandre Quemy, Przemysław Klocek +2Sentence Embeddings Geometric Representation Learning
IBM Automation and AI, Krakow, Poland · Hother, Krakow, Poland · University of Warsaw +1
Learning 4D Geometric Priors for Inference-Efficient World Action Models
Jul 6, 2026Jianjun Zhang, Jian Zhu, Taiyi Su +4World Models for Robotics Action-Conditioned World Models
Tongji University · AIRC, Midea Group
Geometry-Aware Motion Latents for Learning Robust Manipulation Policies
Jul 6, 2026Yunchao Zhang, Yijia Weng, Ruizhe Liu +3Latent Action Learning Geometric Representation Learning
Department of Computer Science, The University of Hong Kong, Hong Kong, China · Department of Computer Science, Stanford University, Stanford, California, USA · Department of Data Science and AI, Monash University, Melbourne, Australia
Geometry of Ordinal Representations in Language Models
Jul 5, 2026Saksham Bassi, Sharvi TomarGeometric Representation Learning Representation Geometry in Language Models
*Equal contribution
Geometry-Aware Infrastructure-Anchored Denoiser for UWB Sensing and Work-Zone Reconstruction
Jul 5, 2026Weizhe Tang, Jiaxi Liu, Junwei you +53D Reconstruction Geometric Representation Learning
Department of Civil & Environmental Engineering, University of Wisconsin-Madison, Madison, 53706, Wisconsin, United States · Department of Civil and Architectural Engineering and Construction Management, University of Wyoming, Laramie, 82071, Wyoming, United States · School of Computer Science and Technology, Chongqing University of Posts and Telecommunications, Chongqing, 400065, China
GeoMix: Descriptor-Free Visual Localization via Global Context and Multi-Detector Training
Jul 2, 2026Yejun Zhang, Xinjue Wang, Zihan Wang +2Visual Place Recognition Camera Pose Estimation
Aalto University, Espoo, Finland · Tampere University, Tampere, Finland · University of Oulu, Oulu, Finland
Understanding Geometric Representations in Self-Supervised Vision Transformers via Subspace Intervention
Jul 2, 2026Weichen Zhou, Yawen Zou, Chunzhi Gu +3Neural Representation Geometry Linear Probing
University of Toyama, Toyama, Japan · University of Fukui, Fukui, Japan · Chukyo University, Aichi, Japan +1
Finite-Lag Operator Geometry of Recurrent Representations
Jul 2, 2026Kanishka ReddyNeural Representation Geometry Recurrent Neural Networks
Department of Applied Mathematics University of Washington Seattle, WA 98105
I\textsuperscript{2}RiMA: Spectral Riemannian Representation with Temporal Attention for Mental Stress Detection based on EEG Signals
Jul 1, 2026Cheng He, Kunyu Peng, Shangen Han +3Cross-Subject EEG Decoding Electroencephalography
Laboratory of Neural Computing and Intelligent Perception, College of Information Engineering, Capital Normal University, Beijing, China · Karlsruhe Institute of Technology, Germany · School of Psychology, Capital Normal University, Beijing, China
HieDG: A Hierarchical Discrete Geometry-Guided Framework for Multi-Animal Tracking
Jul 1, 2026Chenxun Deng, Zhongde Zhang, Ye Yuan +7Geometric Representation Learning Wildlife Monitoring
State Key Laboratory of Brain Cognition and Brain-inspired Intelligence Technology, Institute of Automation, Chinese Academy of Sciences · School of Artificial Intelligence, University of Chinese Academy of Sciences · School of Technology, Beijing Forestry University +6
Deep Spectral Models for Robust Dental Shape Generation
Jun 30, 2026Tibor Kubík, François Guibault, Michal Španěl +1Geometric Representation Learning Generative Modeling
Polytechnique Montr´eal, Montr´eal, Canada · Brno University of Technology, Brno, Czech Republic
Self-supervised Geometry Reasoning for LiDAR Simultaneous Localization and Mapping
Jun 29, 2026Jiwoo Kim, Jinwoo Lee, Woojae Shin +2Simultaneous Localization and Mapping Self-Supervised Learning
Korea Advanced Institute of Science and Technology (KAIST), Daejeon, Republic of Korea · Daegu Gyeongbuk Institute of Science and Technology (DGIST), Daegu, Republic of Korea
From Dataset Spectral Geometry to Network Weights: A Geometry-Aware Initialization for Sigmoidal MLPs in Image Classification
Jun 26, 2026Yi-Shan ChuMultilayer Perceptrons Neural Network Initialization
Institute of Statistical Science, Academia Sinica, Taipei, Taiwan (R.O.C.)
GRAFT: Graph-Based Affordance Transfer via Part Correspondence
Jun 23, 2026Mengying Lin, Utkarsh Mishra, Ajay Mandlekar +1Zero-Shot Robotic Manipulation Generalization in Robotic Manipulation
Georgia Institute of Technology · NVIDIA
VistaRef: Boosting Visual Spatial Orientation Awareness for Pointing-to-Object Detection
Jun 23, 2026Ling Li, Zhizhen Cai, Xinkun Wu +4Visual Spatial Reasoning Detection Transformer
Tsinghua University Beijing Shi, China · Beijing University of Posts and Telecommunications Beijing Shi, China · Tsinghua Shenzhen International Graduate School Shenzhen, Guangdong, China +1
REDI-Match: Rotation-Equivariant Distillation for Efficient and Robust Dense Matching
Jun 23, 2026Yinji Ge, Guixu Zheng, Wulong Guo +5Vision Foundation Model Adaptation Geometric Representation Learning
1Tsinghua University · 2Southern University of Science and Technology · 3Beihang University +1
MAPS: Multi-Anchor Projection Similarity for Joint Vision-Language Geo-Localization
Jun 21, 2026Yutong Hu, Siyuan Tan, Shaocheng Yan +3Cross-View Geo-Localization Geometric Representation Learning
School of Remote Sensing and Information Engineering, Wuhan University, Wuhan 430079, China
Space Is Intelligence: Neural Semigroup Superposition for Riemannian Metric Generation
Jun 17, 2026Chenghao XuGeometric Representation Learning Metric Learning
School of Artificial Intelligence and Robotics and National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University, Changsha, Hunan, China
Deep Image Prototype Learning with Geometric Heat-Kernel Priors
Jun 17, 2026Jiarui Xing, Tal Zeevi, Nian Wu +1Medical Image Analysis Prototype Learning
Yale School of Medicine, New Haven CT, 06510, USA · University of Virginia, Charlottesville, VA, 22903, USA · Harvard Medical School, Boston MA, 02115, USA
What Your Model Threw Away and Why You'll Want It Back: Masking, Fingerprinting, and Privacy from Discarded Geometry
Jun 17, 2026Zachary P. BradshawGeometric Representation Learning Lie Group Methods
QodeX Quantum, Inc. Chicago, IL
Compact Geometric Representations of Hierarchies
Jun 16, 2026Prashant Gokhale, Piotr Indyk, Yuhao Liu +3Hierarchical Representation Learning Geometric Representation Learning
UW-Madison · MIT
Learning the Geometry of Data: A Mathematical Review of Shape Space Analysis
Jun 15, 2026Gary P. T. Choi, Khanh Dao Duc, Shira Faigenbaum-Golovin +6Statistical Shape Modeling Geometric Representation Learning
Department of Mathematics, The Chinese University of Hong Kong, Hong Kong SAR · Department of Mathematics, University of British Columbia, Vancouver, BC V6T 1Z4, Canada · Department of Mathematics, Bar-Ilan University, Ramat-Gan, Israel +6

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Dual-Level Atomic and Coordination Geometry Learning for Crystal Property Prediction Using Graph Neural Networks

Sanjay Chakraborty

Department of Computer and Information Science (IDA), REAL, AIICS, Linköping University, Linköping, Sweden

Graph Neural Networks Materials Property Prediction Geometric Representation Learning Computational Materials Science

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Accurate prediction of crystal properties remains a key challenge in computational materials science. While graph neural networks (GNNs) such as CGCNN, MEGNet, ALIGNN, and SchNet have shown strong performance, they primarily represent crystals at the atomic level and implicitly learn local chemical environments through message passing. However, many material properties are governed by coordination polyhedra, the fundamental structural units formed by atoms and their neighboring atoms. To address this limitation, we propose the Coordination Polyhedron Graph Network (CPGN), a multi-scale GNN that jointly learns atomic, bond, and coordination-polyhedron representations. CPGN constructs three coupled graphs: an atom graph encoding elemental and bonding information, a line graph capturing angular interactions, and a coordination polyhedron graph describing Voronoi-derived local environments through corner-, edge-, and face-sharing relationships. Physically meaningful geometric descriptors are incorporated for each polyhedron, while an interleaved message-passing mechanism with bidirectional cross-attention enables effective information exchange across structural levels. Extensive evaluations on the Materials Project, JARVIS-DFT, and QM9 benchmark datasets demonstrate that CPGN outperforms existing state-of-the-art GNN models. It achieves a formation-energy MAE of 0.060 eV/atom and a band-gap MAE of 0.292 eV on the Materials Project, while providing competitive multi-property prediction on JARVIS-DFT and superior HOMO prediction on QM9. The results highlight that explicit modeling of coordination polyhedra improves crystal representation learning and enables accurate, physically interpretable prediction of material properties.

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