RoomLight: A 2.5D Illumination Prior for Indoor Environments

2026-09-23

AI Summary

该研究通过引入一种基于真实室内全景图和估计深度的空间感知光照先验，解决了室内环境光照高度空间变化的问题，提高了室内光照恢复的保真度。

Abstract

Ill-posed inverse problems require priors to constrain the solution space toward plausible outcomes. In inverse rendering, learned priors modeling the distribution of natural illumination improve the recovery of scene properties. However, existing models rely on the distant-illumination assumption, representing lighting as a far-field environment map. This limits their applicability to indoor scenes, where illumination is highly spatially varying due to finite-distance emitters, visibility changes, and parallax, all of which are poorly approximated by a single environment map. To address this, we introduce a spatially-aware illumination prior trained on real-world indoor panoramas and their estimated depth. Our variational autoencoder model learns a compact, optimizable latent space that decodes into HDR radiance and depth, parameterizing an area light emitter for direct integration into standard differentiable rendering pipelines. This design bridges the plausibility guarantees of a learned prior with the gradient flow required for downstream optimization. Crucially, by jointly modeling radiance and depth, our prior captures the spatial structure of indoor illumination, instead of treating the light sources as infinitely distant. We demonstrate that this formulation enables spatially-varying illumination modeling and achieves higher-fidelity recovery of indoor lighting compared to existing approaches. Project page: https://andreead-a.github.io/RoomLight

Problem

• indoor illumination
• spatially-varying
• distant-illumination assumption

Innovation

• spatially-aware illumination prior
• variational autoencoder
• HDR radiance and depth
• area light emitter
• differentiable rendering