mlsanityData to 14 Oct 2026

Neural Fields for NV-Center Inverse Sensing

arXiv 2605.13988 ↗First posted 13 May 2026cs.LG quant-phcited by 0

Not on the map. Why?

Abstract (arXiv)

Inverse problems in scientific sensing are often solved with either hand-designed regularizers or supervised networks trained on simulated labels, yet both can fail when the forward model is nonlinear, spectrally coupled, and physically delicate. We study this issue for noise sensing based on nitrogen-vacancy (NV) centers in diamond, where a quantum sensor measures magnetic-noise spectra generated by sparse spin sources. We show that replacing a common scalar/coherent forward approximation with a tensor power-summed dipolar operator changes the inverse landscape and exposes a center-collapse failure mode in free-density optimization. We propose NeTMY, an amortization-free coordinate neural field coupled to the differentiable NV forward model, with annealed positional encoding, multiscale optimization, sparsity/gating, and spectrum-fidelity losses. Across sparse synthetic reconstructions generated by the corrected operator, NeTMY achieves the best localization and distributional metrics in the tested benchmark. Mechanism experiments show that NeTMY does not directly execute the raw density-space gradient; its parameterization smooths and redistributes updates, mitigating the center-collapse pathology. These results position NV quantum sensing as a useful testbed for physics-faithful neural inverse problems.

Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 39 of its 85 references that are in the map. First posted on arXiv 13 May 2026.

Builds on

39 of 85 references.

References · 39

  1. 2015U-Net: Convolutional Networks for Biomedical Image Segmentationcited by 19,713
  2. 20233D Gaussian Splatting for Real-Time Radiance Field Renderingcited by 6,226
  3. 2020Implicit Neural Representations with Periodic Activation Functionscited by 2,238
  4. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  5. 2015Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networkscited by 2,054
  6. 2019Do ImageNet Classifiers Generalize to ImageNet?cited by 1,515
  7. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
  8. 2017Deep Image Priorcited by 1,098
  9. 2017Deep Reinforcement Learning that Matterscited by 754
  10. 2022Scientific Machine Learning through Physics-Informed Neural Networks: Where we are and What's nextcited by 608
  11. 2019Algorithm Unrolling: Interpretable, Efficient Deep Learning for Signal and Image Processingcited by 421
  12. 2017Learning Deep CNN Denoiser Prior for Image Restorationcited by 408
27 more
  1. 2021AI and the Everything in the Whole Wide World Benchmarkcited by 349
  2. 2017Learning a Variational Network for Reconstruction of Accelerated MRI Datacited by 342
  3. 2017Compressed Sensing using Generative Modelscited by 339
  4. 2016The Little Engine that Could: Regularization by Denoising (RED)cited by 307
  5. 2020Deep Learning Techniques for Inverse Problems in Imagingcited by 291
  6. 2019On instabilities of deep learning in image reconstruction - Does AI come at a cost?cited by 212
  7. 2013Gradient Magnitude Similarity Deviation: A Highly Efficient Perceptual Image Quality Indexcited by 195
  8. 2018Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overviewcited by 160
  9. 2021NeRP: Implicit Neural Representation Learning with Prior Embedding for Sparsely Sampled Image Reconstructioncited by 146
  10. 2021The Benchmark Lotterycited by 136
  11. 2021Deep Equilibrium Architectures for Inverse Problems in Imagingcited by 127
  12. 2018Deep Decoder: Concise Image Representations from Untrained Non-convolutional Networkscited by 120
  13. 2023Implicit Neural Representation in Medical Imaging: A Comparative Surveycited by 90
  14. 2019DeepRED: Deep Image Prior Powered by REDcited by 85
  15. 2017A Review of Convolutional Neural Networks for Inverse Problems in Imagingcited by 81
  16. 2017CNN-Based Projected Gradient Descent for Consistent Image Reconstructioncited by 77
  17. 2021Dynamic CT Reconstruction from Limited Views with Implicit Neural Representations and Parametric Motion Fieldscited by 71
  18. 2020Compressive sensing with un-trained neural networks: Gradient descent finds the smoothest approximationcited by 39
  19. 2024Improved Implicit Neural Representation with Fourier Reparameterized Trainingcited by 36
  20. 2019Algorithmic Guarantees for Inverse Imaging with Untrained Network Priorscited by 32
  21. 2018Structured Local Optima in Sparse Blind Deconvolutioncited by 15
  22. 2023Neural-network-based regularization methods for inverse problems in imagingcited by 14
  23. 2023Single View Refractive Index Tomography with Neural Fieldscited by 8
  24. 2022Machine-learning-enhanced quantum sensors for accurate magnetic field imagingcited by 4
  25. 2025Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fieldscited by 4
  26. 2024Solving Inverse Problems with Model Mismatch using Untrained Neural Networks within Model-based Architecturescited by 3
  27. 2026Neural Field Thermal Tomography: A Differentiable Physics Framework for Non-Destructive Evaluationcited by 2

Built on by

Not cited yet.