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Determining 3D Flow Fields via Multi-camera Light Field Imaging
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Single-view neural illumination estimation and editing for dynamic light field display.

Xuyang Hong1, Jie Xie1,2, Jie Sheng1

  • 1School of Rail Transportation, Soochow University, 8 Jixue Rd., Suzhou, 215100, China.

Light, Science & Applications
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Summary

This study introduces a neural framework for adaptive light field synthesis, enabling photorealistic virtual and augmented reality experiences from sparse data. It accurately estimates and edits scene illumination for high-fidelity digital light fields.

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Area of Science:

  • Computer Vision
  • Computer Graphics
  • Immersive Technologies

Background:

  • Light field rendering is crucial for photorealistic virtual reality (VR), augmented reality (AR), mixed reality (MR), and extended reality (XR).
  • Synthesizing dense views for high-fidelity displays is challenging with sparse observations in dynamic immersive interactions.
  • Generating photometrically consistent views between virtual and real worlds remains a significant hurdle.

Purpose of the Study:

  • To develop a neural illumination estimation and editing framework for adaptive light field synthesis.
  • To enable high-fidelity relighting from any viewpoint by deconstructing baked-in lighting.
  • To create a practical pathway for interactive and adaptive digital light fields.

Main Methods:

  • A hybrid-guided generative network synthesizes plausible dense views from a single sampling view.
  • Intrinsic illumination parameters are explicitly encoded from sparse observations.
  • A complete rendering model guides the synthesis process for photometric consistency.

Main Results:

  • The method achieves low irradiance error (0.2397 W m⁻²) and angular deviation (7.02°).
  • Synthesized images show a 17.0% improvement in Peak Signal-to-Noise Ratio (PSNR).
  • A 51.2% reduction in Learned Perceptual Image Patch Similarity (LPIPS) was observed, indicating enhanced realism.

Conclusions:

  • The framework offers a practical solution for adaptive light field synthesis from sparse data.
  • It enables consistent and high-fidelity relighting for next-generation immersive displays.
  • This work advances interactive digital light field generation for computational imaging.