Implicit Neural Representations

Also known as INR

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Overview

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

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

  1. Subtractive Modulative Network with Learnable Periodic Activations

    Feb 18, 2026Tiou Wang, Zhuoqian Yang, Markus Flierl +2Implicit Neural RepresentationsImage Reconstruction

    School of Electrical Engineering and Computer Science, KTH Royal Institute of Technology, Sweden · School of Computer and Communication Sciences, EPFL, Switzerland

  2. Multi-Class Boundary Extraction from Implicit Representations

    Feb 18, 2026Jash Vira, Andrew Myers, Simon RatcliffeImplicit Neural Representations

  3. Refine Now, Query Fast: A Decoupled Refinement Paradigm for Implicit Neural Fields

    Feb 16, 2026Tianyu Xiong, Skylar Wurster, Han-Wei ShenImplicit Neural RepresentationsNeural Surrogate Modeling

    Department of Computer Science and Engineering Ohio State University Columbus, OH 43210, USA · Adobe San Francisco, CA, USA

  4. Weight Space Representation Learning via Neural Field Adaptation

    Dec 1, 2025Zhuoqian Yang, Mathieu Salzmann, Sabine SüsstrunkImplicit Neural RepresentationsLow-Rank Adaptation

    EPFL

  5. OREN: Octree Residual Network for Real-Time Euclidean Signed Distance Mapping

    Oct 21, 2025Zhirui Dai, Qihao Qian, Tianxing Fan +13D MappingImplicit Neural Representations

    Department of Electrical and Computer Engineering, University of California San Diego, La Jolla, CA 92093, USA

  6. NeMo-map: Neural Implicit Flow Fields for Spatio-Temporal Motion Mapping

    Oct 16, 2025Yufei Zhu, Shih-Min Yang, Andrey Rudenko +3Implicit Neural RepresentationsHuman Trajectory Prediction

    Orebro University, Sweden · Technical University of Munich, Germany · Aalto University, Finland

  7. Temporal Variational Implicit Neural Representations

    Jun 2, 2025Batuhan Koyuncu, Rachael DeVries, Ole Winther +1Irregular Time-Series ModelingMultivariate Time Series Forecasting

    Department of Computer Science, Saarland University · Department of Biology, University of Copenhagen · Novo Nordisk A/S +1

  8. NumGrad-Pull: Numerical Gradient Guided Tri-plane Representation for Surface Reconstruction from Point Clouds

    Nov 26, 2024Ruikai Cui, Binzhu Xie, Shi Qiu +3Implicit Neural Representations3D Reconstruction

    School of Computing, The Australian National University, Australia · Department of Computer Science and Engineering, The Chinese University of Hong Kong, Hong Kong SAR, China · Department of Computer Science & Software Engineering, the University of Western Australia, Australia