A regularization technique that treats diffusion model outputs as a similarity kernel during material optimization in inverse rendering, enabling joint reconstruction of geometry, materials, and illumination that satisfies the rendering equation and generalizes to new lighting.
Luxdit: Lighting estimation with video diffusion transformer.arXiv preprint arXiv:2509.03680, 2025
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
fields
cs.CV 2years
2026 2verdicts
UNVERDICTED 2representative citing papers
AEGIR introduces explicit area-emitter modeling inside a relightable Gaussian Splatting pipeline together with a differentiable deferred renderer using multiple importance sampling and regularization to improve lighting-material decomposition.
citing papers explorer
-
Diffusion-Based Material Regularization for Physics-Based Inverse Rendering
A regularization technique that treats diffusion model outputs as a similarity kernel during material optimization in inverse rendering, enabling joint reconstruction of geometry, materials, and illumination that satisfies the rendering equation and generalizes to new lighting.
-
AEGIR: Modeling Area Emitters for Indoor Inverse Rendering using Gaussian Splatting
AEGIR introduces explicit area-emitter modeling inside a relightable Gaussian Splatting pipeline together with a differentiable deferred renderer using multiple importance sampling and regularization to improve lighting-material decomposition.