mlsanityData to 14 Oct 2026

Gravitationally Lensed Black Hole Emission Tomography

arXiv 2204.03715 ↗First posted 7 Apr 2022cs.CV astro-ph.IMcited by 22

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Abstract (arXiv)

Measurements from the Event Horizon Telescope enabled the visualization of light emission around a black hole for the first time. So far, these measurements have been used to recover a 2D image under the assumption that the emission field is static over the period of acquisition. In this work, we propose BH-NeRF, a novel tomography approach that leverages gravitational lensing to recover the continuous 3D emission field near a black hole. Compared to other 3D reconstruction or tomography settings, this task poses two significant challenges: first, rays near black holes follow curved paths dictated by general relativity, and second, we only observe measurements from a single viewpoint. Our method captures the unknown emission field using a continuous volumetric function parameterized by a coordinate-based neural network, and uses knowledge of Keplerian orbital dynamics to establish correspondence between 3D points over time. Together, these enable BH-NeRF to recover accurate 3D emission fields, even in challenging situations with sparse measurements and uncertain orbital dynamics. This work takes the first steps in showing how future measurements from the Event Horizon Telescope could be used to recover evolving 3D emission around the supermassive black hole in our Galactic center.

Key facts. Cited by 22 papers in the map (data through 14 Oct 2026). Builds on 12 of its 50 references that are in the map. First posted on arXiv 7 Apr 2022.

Builds on

12 of 50 references.

References · 12

  1. 2014Adam: A Method for Stochastic Optimizationcited by 37,240
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  3. 2020Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domainscited by 2,200
  4. 2018Occupancy Networks: Learning 3D Reconstruction in Function Spacecited by 2,041
  5. 2020Nerfies: Deformable Neural Radiance Fieldscited by 1,198
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Cited by year (2026 to 14 Oct)

Citing papers per year: 2022: 3; 2023: 6; 2024: 3; 2025: 7; 2026: 3.

22 papers.

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  1. 2022NeRFPlayer: A Streamable Dynamic Scene Representation with Decomposed Neural Radiance Fieldscited by 152
  2. 2023SparseGS: Sparse View Synthesis using 3D Gaussian Splattingcited by 134
  3. 2024Advances in 3D Generation: A Surveycited by 63
  4. 2024SITCOM: Step-wise Triple-Consistent Diffusion Sampling for Inverse Problemscited by 24
  5. 2022Exact-NeRF: An Exploration of a Precise Volumetric Parameterization for Neural Radiance Fieldscited by 15
  6. 2025Reconstructing 4D Spatial Intelligence: A Surveycited by 14
  7. 2023Single View Refractive Index Tomography with Neural Fieldscited by 8
  8. 2023NeRFs: The Search for the Best 3D Representationcited by 7
  9. 2023Radar Fields: An Extension of Radiance Fields to SARcited by 7
  10. 2022Strong Lensing Source Reconstruction Using Continuous Neural Fieldscited by 6
  11. 2023Orbital Polarimetric Tomography of a Flare Near the Sagittarius A* Supermassive Black Holecited by 5
  12. 2025Revealing the 3D Cosmic Web through Gravitationally Constrained Neural Fieldscited by 4
10 more
  1. 2025UGoDIT: Unsupervised Group Deep Image Prior Via Transferable Weightscited by 3
  2. 2023Recovering a Molecule's 3D Dynamics from Liquid-phase Electron Microscopy Moviescited by 2
  3. 2026Dynamic Black-hole Emission Tomography with Physics-informed Neural Fieldscited by 1
  4. 2024Success Probability in Multi-View Imagingcited by 0
  5. 2025Spectra-to-Structure and Structure-to-Spectra Inference Across the Periodic Tablecited by 0
  6. 2025Learning Null Geodesics for Gravitational Lensing Rendering in General Relativitycited by 0
  7. 2025Revealing Fine Structure in Protoplanetary Disks with Physics Constrained Neural Fieldscited by 0
  8. 2025NERD: Network-Regularized Diffusion Sampling For 3D Computed Tomographycited by 0
  9. 2026A Principled Self-Referenced Early Stopping Approach for Deep Image Priorcited by 0
  10. 2026Image Fidelity is Not Field Fidelity: Joint Thermodynamic Reconstruction and Error Localization in Neural Tomographycited by 0