mlsanityData to 7 Oct 2026

Curvature-Aware Training for Coordinate Networks

arXiv 2305.08552 ↗First posted 15 May 2023cs.CVcited by 8

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Abstract (arXiv)

Coordinate networks are widely used in computer vision due to their ability to represent signals as compressed, continuous entities. However, training these networks with first-order optimizers can be slow, hindering their use in real-time applications. Recent works have opted for shallow voxel-based representations to achieve faster training, but this sacrifices memory efficiency. This work proposes a solution that leverages second-order optimization methods to significantly reduce training times for coordinate networks while maintaining their compressibility. Experiments demonstrate the effectiveness of this approach on various signal modalities, such as audio, images, videos, shape reconstruction, and neural radiance fields.

Key facts. Cited by 8 papers in the map (data through 7 Oct 2026). Builds on 38 of its 53 references that are in the map. First posted on arXiv 15 May 2023.

Builds on

38 of 53 references.

References · 38

  1. 2014Adam: A Method for Stochastic Optimizationcited by 36,960
  2. 2019DeepSDF: Learning Continuous Signed Distance Functions for Shape Representationcited by 2,682
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,214
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,170
  5. 2021Plenoxels: Radiance Fields without Neural Networkscited by 1,607
  6. 2020pixelNeRF: Neural Radiance Fields from One or Few Imagescited by 1,574
  7. 2020D-NeRF: Neural Radiance Fields for Dynamic Scenescited by 1,459
  8. 2018On the Spectral Bias of Neural Networkscited by 1,321
  9. 2022TensoRF: Tensorial Radiance Fieldscited by 1,275
  10. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,194
  11. 2018Learning Implicit Fields for Generative Shape Modelingcited by 1,060
  12. 2021Direct Voxel Grid Optimization: Super-fast Convergence for Radiance Fields Reconstructioncited by 961
26 more
  1. 2019Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representationscited by 864
  2. 2019PIFu: Pixel-Aligned Implicit Function for High-Resolution Clothed Human Digitizationcited by 818
  3. 2015Optimizing Neural Networks with Kronecker-factored Approximate Curvaturecited by 776
  4. 2020Implicit Geometric Regularization for Learning Shapescited by 707
  5. 2019Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelinescited by 672
  6. 2021KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPscited by 672
  7. 2021BARF: Bundle-Adjusting Neural Radiance Fieldscited by 641
  8. 2021NICE-SLAM: Neural Implicit Scalable Encoding for SLAMcited by 586
  9. 2020Learning Continuous Image Representation with Local Implicit Image Functioncited by 550
  10. 2018Shampoo: Preconditioned Stochastic Tensor Optimizationcited by 413
  11. 2019SAL: Sign Agnostic Learning of Shapes from Raw Datacited by 389
  12. 2016Thingi10K: A Dataset of 10,000 3D-Printing Modelscited by 182
  13. 2020DeRF: Decomposed Radiance Fieldscited by 163
  14. 2023Handbook of Convergence Theorems for (Stochastic) Gradient Methodscited by 158
  15. 2019NASA: Neural Articulated Shape Approximationcited by 149
  16. 2020Adversarial Generation of Continuous Imagescited by 135
  17. 2021Beyond Periodicity: Towards a Unifying Framework for Activations in Coordinate-MLPscited by 134
  18. 20213D Neural Scene Representations for Visuomotor Controlcited by 110
  19. 2022GARF: Gaussian Activated Radiance Fields for High Fidelity Reconstruction and Pose Estimationcited by 86
  20. 2023Factor Fields: A Unified Framework for Neural Fields and Beyondcited by 58
  21. 2015A Linearly-Convergent Stochastic L-BFGS Algorithmcited by 39
  22. 2021CoIL: Coordinate-based Internal Learning for Imaging Inverse Problemscited by 33
  23. 2021Spline Positional Encoding for Learning 3D Implicit Signed Distance Fieldscited by 21
  24. 2022Trading Positional Complexity vs. Deepness in Coordinate Networkscited by 16
  25. 2019Optimal Function Approximation with Relu Neural Networkscited by 9
  26. 2017Stochastic L-BFGS: Improved Convergence Rates and Practical Acceleration Strategiescited by 2

Built on by

Cited by year

Citing papers per year: 2024: 4; 2025: 4.

8 papers.

  1. 2024A Sampling Theory Perspective on Activations for Implicit Neural Representationscited by 23
  2. 2024From Activation to Initialization: Scaling Insights for Optimizing Neural Fieldscited by 11
  3. 2025Optimizing Rank for High-Fidelity Implicit Neural Representationscited by 7
  4. 2024Preconditioners for the Stochastic Training of Neural Fieldscited by 5
  5. 2025Enhancing Transformers Through Conditioned Embedded Tokenscited by 3
  6. 2025Data Denoising and Derivative Estimation for Data-Driven Modeling of Nonlinear Dynamical Systemscited by 1
  7. 2025From Tables to Signals: Revealing Spectral Adaptivity in TabPFNcited by 1
  8. 2024Architectural Strategies for the optimization of Physics-Informed Neural Networkscited by 0