AI 中文总结
本文针对室内场景重光照难题，提出FreeLit无配对框架，利用物理引导光照先验及重光照引导的固有稳定策略，结合面向可控性的评估指标，实现稳定、物理一致且可控的重光照，提升低光照场景下的鲁棒性，无需配对监督。
AI 中文摘要
基于图像的室内场景重光照由于杂乱几何与局部光照间复杂相互作用而仍具挑战性，需精确建模光的位置、颜色和强度。现有数据驱动方法通过配对多光照数据集隐式学习此关系，但数据成本高且难以扩展。逆渲染方法虽减少数据依赖，但在挑战性条件下缺乏固有估计的鲁棒性。本文提出FreeLit，一个用于可控室内重光照的无配对框架，通过内在场景属性构建物理引导光照先验，生成结构化光照图和伪重光照图像以引导基于扩散的合成。为解决固有估计中的不稳定性，引入重光照引导的固有稳定策略。还提出面向可控性的评估指标。实验表明FreeLit实现稳定、物理一致且可控的重光照，在低光照室内场景中鲁棒性提高且无需配对监督。
英文摘要
Image-based indoor scene relighting remains challenging due to the complex interplay between cluttered geometry and local illumination, requiring precise modeling of light position, color, and intensity. Existing data-driven methods implicitly learn this relationship via paired multi-illumination datasets. Nevertheless, this data is costly and fails to scale, which is essential for accurate light-source-level control. Conversely, inverse-rendering methods reduce the data dependency by incorporating physical priors; however, they lack the robustness of intrinsic estimation in challenging conditions. In this paper, we present FreeLit, a paired-free framework for controllable indoor relighting that explicitly manipulates light-source location, color, and intensity. Instead of relying on paired supervision, we construct a physics-guided illumination prior from intrinsic scene properties, generating a structured lightmap along with a pseudo-relit image to guide diffusion-based synthesis. To address instability in intrinsic estimation, especially in low-light scenes, we introduce a relighting-guided intrinsic stabilization strategy that enforces illumination-invariant reflectance through structure-aware distillation and consistency constraints. Furthermore, we propose controllability-oriented evaluation metrics to quantify alignment with user-specified illumination color and intensity. Experimental results demonstrate that FreeLit achieves stable, physically consistent, and controllable relighting, with improved robustness in low-light indoor scenes, without requiring paired supervision.
CommentsUpdated to the ACM Multimedia 2026 camera-ready version
