Implicit Neural Representations

Also known as INR

Implicit neural representations (INRs) learn continuous functions from coordinates to values, such as image colors, 3D geometry, motion, or physical measurements. Common approaches use coordinate-based MLPs with sinusoidal or Fourier features, multiresolution grids, tri-planes, or hybrid designs that combine neural decoders with explicit spatial structure. Researchers use them for reconstruction, compression, super-resolution, rendering, and scientific inverse problems, where continuous querying can handle irregular samples and varying resolutions. A central challenge is balancing detail and accuracy against fitting time, memory, and query cost; adaptive representations and learned priors help, but generalization remains task-dependent, and explicit methods can sometimes be more efficient or accurate.

Latest papers

188 papers25 papers in the last four weeks, up 4% on the four weeks before. 0.1% of all new papers.

IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation
May 15, 2026Yuqi Wu, Tianyu Hu, Wenzhao Zheng +43D ViTs Implicit Neural Representations
Intelligent Vision Group, Tsinghua University
RaPD: Resolution-Agnostic Pixel Diffusion via Semantics-Enriched Implicit Representations
May 15, 2026Yanhao Ge, Shanyan Guan, Weihao Wang +2Implicit Neural Representations Neural Rendering
College of Electronic and Information Engineering, Tongji University · vivo Mobile Communication Co., Ltd. · Nanjing University.
Neural Fields for NV-Center Inverse Sensing
May 13, 2026Zhixuan Zhao, Tao Zhong, Yixun Hu +2Implicit Neural Representations Inverse Problems
Princeton University · Tsinghua University
Generative Motion In-betweening by Diffusion over Continuous Implicit Representations
May 12, 2026Shiyu Fan, Paul Henderson, Edmond S. L. HoImplicit Neural Representations Latent Diffusion Models
School of Computing Science, University of Glasgow, United Kingdom.
Spectral Energy Centroid: a Metric for Improving Performance and Analyzing Spectral Bias in Implicit Neural Representations
May 12, 2026Tomasz Dądela, Adam Kania, Maciej Rut +1Implicit Neural Representations Spectral Bias
Jagiellonian University IDEAS Kraków, Poland
INFANiTE: Implicit Neural representation for high-resolution Fetal brain spatio-temporal Atlas learNing from clinical Thick-slicE MRI
May 11, 2026Xiaotian Hu, Mingxuan Liu, Hongjia Yang +10Implicit Neural Representations Longitudinal Medical Image Analysis
Tsinghua University · University of Oxford · Sichuan University
WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain
May 10, 2026Yi Xiao, Qilong Jia, Hang Fan +4Implicit Neural Representations Inference-Time Optimization
Tsinghua University · MBZUAI · Columbia University +2
What Cohort INRs Encode and Where to Freeze Them
May 8, 2026Vasiliki Sideri-Lampretsa, Sophie Starck, Robbie Holland +2Implicit Neural Representations Mechanistic Interpretability
Chair of AI in Healthcare and Medicine, Technical University of Munich, Germany · TUM University Hospital, Munich, Germany · Stanford Center for Artificial Intelligence in Medicine and Imaging, Stanford University, Stanford, CA, USA +3
Is Class Signal Clustered or Routed in Task-Induced Implicit Neural Representation Weight Spaces?
May 8, 2026Xinyi Guo, Mingyi He, Haobin Ding +7Neural Representation Geometry Implicit Neural Representations
South China Normal University · Beijing University of Chemical Technology · Tsinghua University +1
Model-based Dynamic 3D MRI Reconstructions using Neural Fields and Tensor Product Expansions
May 8, 2026Ray Sheombarsing, Max van Riel, David Heesterbeek +2Implicit Neural Representations 3D Medical Imaging
Computational Imaging Group for MRI Therapy & Diagnostics, Department of Radiotherapy, University Medical Center Utrecht, Utrecht, The Netherlands
Render, Don't Decode: Weight-Space World Models with Latent Structural Disentanglement
May 7, 2026Roussel Desmond Nzoyem, Mauro ComiDisentangled Representation Learning Implicit Neural Representations
Department of Computer Science University of Manchester Manchester, M13 9PL · Department of Computer Science University of Bristol Bristol, BS8 1QU
Disentangled Learning Improves Implicit Neural Representations for Medical Reconstruction
May 5, 2026Qing Wu, Xuanyu Tian, Chenhe Du +4Disentangled Representation Learning Implicit Neural Representations
Medical AI Lab, Ant Group · ShanghaiTech University · Shanghai Jiao Tong University
Conditional Neural Field based Reduced Order Model for Dynamic Ditching Load Prediction
May 5, 2026Henning Schwarz, Pyei Phyo Lin, Jens-Peter M. Zemke +1Neural Surrogate Modeling Implicit Neural Representations
Institute for Fluid Dynamics and Ship Theory, Hamburg University of Technology, Am Schwarzenberg-Campus 4, D-21073 Hamburg, Germany · Institute of Mathematics, Hamburg University of Technology, Am Schwarzenberg-Campus 3, D-21073 Hamburg, Germany
Self-Supervised Spatial And Zero-Shot Angular Super-Resolution by Spatial-Angular Implicit Representation For Rotating-View SNR-Efficient Diffusion MRI
May 4, 2026Yinzhe Wu, Hongyu Rui, Fanwen Wang +3Implicit Neural Representations Medical Image Super-Resolution
Department of Bioengineering and I-X, Imperial College London, London, UK · Cardiovascular Research Centre, Royal Brompton Hospital, London, UK
PILIR: Physics-Informed Local Implicit Representation
May 1, 2026Jianfeng Li, Feng Wang, Ke TangNeural PDE Solvers Implicit Neural Representations
School of Computer Science, Wuhan University · School of Computer Science and Technology, Southern University of Science and Technology
From Images2Mesh: A 3D Surface Reconstruction Pipeline for Non-Cooperative Space Objects
Apr 30, 2026Bala Prenith Reddy Gopu, Patrick Quinn, George M. Nehma +4Implicit Neural Representations 3D Reconstruction
Department of Aerospace, Physics and Space Sciences, Florida Institute of Technology, 150 W University Blvd, Melbourne, 32901, FL, USA · Creare LLC, Hanover, 03755, NH, USA
PEPS: Positional Encoding Projected Sampling -- Extended
Apr 27, 2026Guillaume Perez, Janarbek Matai, Takahiro HaradaImplicit Neural Representations Positional Encoding
Advanded Micro Devices
Fast Amortized Fitting of Scientific Signals Across Time and Ensembles via Transferable Neural Fields
Apr 21, 2026Sophia Zorek, Kushal Vyas, Yuhao Liu +3Implicit Neural Representations Amortized Inference
Rice University · Argonne National Laboratory
Implicit neural representations as a coordinate-based framework for continuous environmental field reconstruction from sparse ecological observations
Apr 20, 2026Agnieszka Pregowska, Hazem M. KalajiImplicit Neural Representations
Institute of Fundamental Technological Research, Polish Academy of Sciences, Pawinskiego 5B, Warsaw, 02-106, Poland · Department of Plant Physiology, Institute of Biology, Warsaw University of Life Science, 159 Nowoursynowska St, Warsaw, 02-776, Poland · Institute of Technology and Life Sciences Falenty, Al. Hrabska 3, Raszyn, 05-090, Poland
Frequency-Decomposed INR for NIR-Assisted Low-Light RGB Image Denoising
Apr 18, 2026Ligen Shi, Zengyu Pang, Chang Liu +2Implicit Neural Representations Frequency-Domain Image Processing
College of Computer Science (College of Software), Inner Mongolia University, Hohhot, 010021, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China · Institute of Computational Imaging, Beijing Information Science and Technology University, Beijing 102206, China
FLARE: A Data-Efficient Surrogate for Predicting Displacement Fields in Directed Energy Deposition
Apr 17, 2026Kittipong Thiamchaiboonthawee, Ghadi Nehme, Ram Mohan Telikicherla +5Neural Surrogate Modeling Implicit Neural Representations
Massachusetts Institute of Technology, Cambridge, MA, USA · GE Vernova Advanced Research Center, Niskayuna, NY, USA
Implicit Neural Representations: A Signal Processing Perspective
Apr 16, 2026Dhananjaya Jayasundara, Vishal M. PatelImplicit Neural Representations Spectral Bias
Department of Electrical and Computer Engineering, Johns Hopkins University, Baltimore, MD 21218 USA
SAND: Spatially Adaptive Network Depth for Fast Sampling of Neural Implicit Surfaces
Apr 15, 2026Chuanxiang Yang, Junhui Hou, Yuan Liu +5Implicit Neural Representations Efficient Neural Network Inference
Shandong University, China · City University of Hong Kong, China · Hong Kong University of Science and Technology, China +2
INST-Align: Implicit Neural Alignment for Spatial Transcriptomics via Canonical Expression Fields
Apr 13, 2026Bonian Han, Cong Qi, Przemyslaw Musialski +1Batch Effect Correction Spatial Transcriptomics
New Jersey Institute of Technology, Newark, NJ, USA
NeuVolEx: Implicit Neural Features for Volume Exploration
Apr 13, 2026Haill An, Suhyeon Kim, Donghyuk Choo +1Implicit Neural Representations 3D Representation Learning
School of Computing, Gachon University, Republic of Korea.
Location Is All You Need: Continuous Spatiotemporal Neural Representations of Earth Observation Data
Apr 8, 2026Mojgan Madadikhaljan, Jonathan Prexl, Isabelle Wittmann +2Geospatial Representation Learning Implicit Neural Representations
University of the Bundeswehr Munich · IBM Research – Europe · Columbia University +1
CWRNN-INVR: A Coupled WarpRNN based Implicit Neural Video Representation
Apr 8, 2026Yiyang Li, Yanbo Gao, Shuai Li +5Video Representation Learning Implicit Neural Representations
School of Control Science and Engineering, Shandong University, and Key Laboratory of Machine Intelligence and System Control, Ministry of Education, Jinan 250100, China. · School of Software, Shandong University, Jinan 250100, China, and also with Shandong University-WeiHai Research Institute of Industrial Technology, Weihai 264209, China. · University of Electronic Science and Technology of China, Sichuan, China. +1
Adaptive Local Frequency Filtering for Fourier-Encoded Implicit Neural Representations
Apr 3, 2026Ligen Shi, Jun Qiu, Yuhang Zheng +2Implicit Neural Representations Fourier Feature Embeddings
College of Computer Science (College of Software), Inner Mongolia University, Hohhot, 010021, China · Institute of Computational Imaging, Beijing Information Science and Technology University, Beijing, 102206, China · School of Mathematics and Statistics, Wuhan University, Wuhan, 430072, China
Hi-LOAM: Hierarchical Implicit Neural Fields for LiDAR Odometry and Mapping
Apr 2, 2026Zhiliu Yang, Jianyuan Zhang, Lianhui Zhao +2Simultaneous Localization and Mapping LiDAR Odometry
School of Information Science and Engineering, Yunnan University, Kunming, Yunnan 650500, China. · Yunnan Key Laboratory of Intelligent Systems and Computing, Yunnan University, Kunming, Yunnan 650500, China. · School of Information and Electronics, Beijing Institute of Technology, Beijing 100081, China.
The Surprising Effectiveness of Noise Pretraining for Implicit Neural Representations
Mar 30, 2026Kushal Vyas, Alper Kayabasi, Daniel Kim +3Implicit Neural Representations Neural Network Initialization
Rice University · University of California, Riverside

May 15, 2026J/K move · Enter open · S save

IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation

Yuqi Wu, Tianyu Hu, Wenzhao Zheng, Yuanhui Huang+3

Intelligent Vision Group, Tsinghua University

3D ViTs Implicit Neural Representations Multi-View 3D Reconstruction Novel View Synthesis 3D Representation Learning

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Reconstructing coherent 3D geometry and appearance from unposed multi-view images is a fundamental yet challenging problem in computer vision. Most existing visual geometry foundation models predict explicit geometry by regressing pixel-aligned pointmaps, often suffering from redundancy and limited geometric continuity. We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images. This formulation learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions, retrieving local features to predict signed distance (SDF) values and colors using lightweight decoders. It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints. We train IVGT via multi-dataset joint optimization with 2D supervision and 3D geometric regularization. IVGT demonstrates generalization across scenes and achieves strong performance on various tasks, including mesh and point cloud reconstruction, novel view synthesis, depth and surface normal estimation, and camera pose estimation.