Neural Radiative Transfer for Inverse Rendering
ssh4net · x · 2026-07-16
The paper "Volumetric Inverse Rendering via Neural Radiative Transfer" explores recovering the optical properties of participating media from images.
Core Idea
- Existing methods either rely on differentiable stochastic light transport simulations, which are complex to engineer, or simplified models that struggle to capture global illumination.
- The authors propose a representation combining complete physical light transport with general neural optimization.
- Both the optical properties of the medium and the full light field are represented as neural fields and estimated via joint optimization.
Method Details
- Uses the local differential form of the radiative transfer equation to construct a residual objective, enforcing global illumination consistency.
- Overlays a volume rendering term along the primary ray to mitigate low-frequency bias.
Results & Extensions
- Capable of reconstructing spatially varying, color-dependent scattering, absorption, and phase function parameters from multi-view images.
- Beyond reconstruction, the framework supports learning generative models of participating media under physical optical constraints.
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