发表机构
Friedrich-Alexander-Universität Erlangen-Nürnberg（弗里德里希-亚历山大-埃尔朗根-纽伦堡大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对室内光照空间变化问题，提出基于变分自编码器的2.5D光照先验，联合建模辐射度和深度，实现空间变化光照建模，提升室内光照恢复保真度。
AI 中文摘要
病态逆问题需要先验来将解空间约束到合理的结果。在逆渲染中，学习到的建模自然光照分布的先验可以改善场景属性的恢复。然而，现有模型依赖于远距离光照假设，将光照表示为远场环境贴图。这限制了它们对室内场景的适用性，因为在室内场景中，由于有限距离的发光体、可见性变化和视差，光照在空间上变化很大，而这些都无法用单一环境贴图很好地近似。为了解决这个问题，我们引入了一个在真实世界室内全景图及其估计深度上训练的空间感知光照先验。我们的变分自编码器模型学习了一个紧凑、可优化的潜在空间，该空间解码为HDR辐射度和深度，参数化一个面光源发射器，以便直接集成到标准可微渲染管线中。这种设计将学习先验的合理性保证与下游优化所需的梯度流连接起来。关键的是，通过联合建模辐射度和深度，我们的先验捕捉了室内光照的空间结构，而不是将光源视为无限远。我们证明，这种公式能够实现空间变化的光照建模，并且与现有方法相比，能够实现更高保真度的室内光照恢复。项目页面：https://andreead-a.github.io/RoomLight
英文摘要
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
