└── README.md /README.md: -------------------------------------------------------------------------------- 1 | # Awesome Implicit Neural Representations [![Awesome](https://camo.githubusercontent.com/8693bde04030b1670d5097703441005eba34240c32d1df1eb82a5f0d6716518e/68747470733a2f2f63646e2e7261776769742e636f6d2f73696e647265736f726875732f617765736f6d652f643733303566333864323966656437386661383536353265336136336531353464643865383832392f6d656469612f62616467652e737667)](https://github.com/sindresorhus/awesome) 2 | 3 | > A curated list of resources on implicit neural representations, inspired by [awesome-implicit-representations](https://github.com/vsitzmann/awesome-implicit-representations). 4 | 5 | 6 | 7 | 8 | 9 | ## Motivation 10 | 11 | **I**mplicit **N**eural **R**epresentations **(INR)** have long been an intriguing topic, and in recent years, several classic works on implicit neural representations have emerged. However, there has been a lack of a comprehensive repository to organize these works. Some similar repositories stopped updating in recent years, leaving the latest papers on implicit neural representations uncollected. Therefore, I have created this repository with the hope of organizing the development trajectory of this field as I read through the latest papers. 12 | 13 | 14 | 15 | ### Survey 16 | 17 | - **[2023 ICCV]** [Implicit Neural Representation in Medical Imaging: A Comparative Survey](https://arxiv.org/abs/2307.16142) Amirali Molaei et al. 18 | 19 | 20 | 21 | ## Paper 22 | 23 | 24 | 25 | ### Theory 26 | 27 | - **[2018 NeurIPS]** [Neural Tangent Kernel: Convergence and Generalization in Neural Networks](https://arxiv.org/abs/1806.07572) Arthur Jacot et al. 28 | 29 | 30 | 31 | 32 | 33 | ### New Architecture 34 | 35 | - **[2024 CVPR]** [Is Vanilla MLP in Neural Radiance Field Enough for Few-shot View Synthesis?](http://arxiv.org/abs/2403.06092) Hanxin Zhu et al. 36 | - **[2024 CVPR]** [SketchINR: A First Look into Sketches as Implicit Neural Representations](https://arxiv.org/abs/2403.09344) Hmrishav Bandyopadhyay et al. 37 | 38 | 39 | 40 | 41 | 42 | ### Faster training 43 | 44 | - **[2024 arXiv]** [FreSh: Frequency Shifting for Accelerated Neural Representation Learning](https://arxiv.org/abs/2410.05050) Adam Kania et al. 45 | 46 | 47 | 48 | 49 | 50 | ### High Representation Ability 51 | 52 | - :star: **[2020 NeurIPS]** [Implicit Neural Representations with Periodic Activation Functions](http://arxiv.org/abs/2006.09661) Vincent Sitzmann et al. 53 | - **[2020 NeurIPS]** [Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains](http://arxiv.org/abs/2006.10739) Matthew Tancik et al. 54 | - **[2024 CVPR]** [Improved Implicit Neural Representation with Fourier Reparameterized Training](http://arxiv.org/abs/2401.07402) Shuhang Gu et al. 55 | - **[2023 CVPR]** [Regularize implicit neural representation by itself](http://arxiv.org/abs/2303.15484) Zhemin Li et al. 56 | 57 | 58 | 59 | 60 | 61 | ### Data Representation (Image) 62 | 63 | - **[2021 CVPR]** [Learned Initializations for Optimizing Coordinate-Based Neural Representations](http://arxiv.org/abs/2012.02189) Matthew Tancik et al. 64 | 65 | 66 | 67 | ### Data Representation (3D Scene) 68 | 69 | - :star: **​[2020 ECCV]** [NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis](http://arxiv.org/abs/2003.08934) Pratul P. Srinivasan et al. 70 | - **[2022 ECCV]** [R2L: Distilling Neural Radiance Field to Neural Light Field for Efficient Novel View Synthesis](http://arxiv.org/abs/2203.17261) Huan Wang et al. 71 | - **[2022 ECCV]** [TensoRF: Tensorial Radiance Fields](https://arxiv.org/abs/2203.09517) Anpei Chen et al. 72 | - **[2022 CVPR]** [Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstruction](https://arxiv.org/abs/2111.11215) Cheng Sun et al. 73 | - **[2022 SIGGRAPH]** [Instant Neural Graphics Primitives with a Multiresolution Hash Encoding](https://arxiv.org/abs/2201.05989) Thomas Müller et al. 74 | 75 | 76 | ### Data Representation (Video) 77 | 78 | 79 | 80 | 81 | 82 | ### Data Representation (Neural Network) 83 | 84 | - **[2023 ICLR]** [NeRN -- Learning Neural Representations for Neural Networks](https://arxiv.org/abs/2212.13554) Maor Ashkenazi et al. 85 | - **[2025 arXiv]** [Enhancing Accuracy and Parameter-Efficiency of Neural Representations for Network Parameterization](https://arxiv.org/abs/2407.00356) Hongjun Choi et al. 86 | 87 | 88 | 89 | ### Data Compression 90 | 91 | - **[2021 ICLR]** [COIN: COmpression with Implicit Neural Representations](https://arxiv.org/abs/2103.03123) Emilien Dupont et al. 92 | 93 | 94 | 95 | 96 | 97 | 98 | 99 | 100 | 101 | ## Related repo 102 | 103 | - [Awesome NeRF](https://github.com/awesome-NeRF/awesome-NeRF) 104 | - [Awesome Implicit Representations](https://github.com/vsitzmann/awesome-implicit-representations) 105 | - [Awesome Implicit NeRF Robotics](https://github.com/zubair-irshad/Awesome-Implicit-NeRF-Robotics) 106 | - [Awesome Implicit Neural Representations in Medical Imaging](https://github.com/xmindflow/Awesome-Implicit-Neural-Representations-in-Medical-imaging) 107 | 108 | 109 | 110 | 111 | 112 | ## Related Workshop 113 | 114 | - [INRV](https://inrv.github.io/) 115 | 116 | --------------------------------------------------------------------------------