Recent radiance-field-based reconstruction methods, such as NeRF and 3DGS, achieve high visual fidelity for indoor scenes, but often break down under scene editing due to baked illumination and the lack of explicit light transport. In contrast, inverse path tracing methods based on mesh representations enforce correct light transport but require highly accurate geometry, making them difficult to apply robustly to real indoor scenes. We present Emission-Aware Gaussians and Path Tracing (EAG-PT), a method for physically based reconstruction and rendering of indoor scenes using a unified 2D Gaussian representation, targeting editable diffuse global illumination. Our approach consists of three key ideas: (1) representing indoor scenes with 2D Gaussians as a transport-friendly geometric proxy that avoids explicit mesh reconstruction; (2) explicitly separating emissive and non-emissive components during reconstruction to support editing; and (3) decoupling reconstruction from final rendering by using efficient single-bounce optimization and high-quality multi-bounce path tracing, respectively. Experiments on synthetic and real indoor scenes show that EAG-PT produces more natural and physically consistent edited renderings than radiance-field reconstructions, while preserving finer geometric detail and avoiding mesh-induced artifacts compared with mesh-based inverse path tracing. These results highlight the potential of our approach for applications such as interior design, XR content creation, and embodied AI.

翻译：近年来基于辐射场的重建方法（如NeRF和3DGS）在室内场景中实现了高视觉保真度，但由于光照烘焙效应及显式光传输机制的缺失，在场景编辑时往往失效。相比之下，基于网格表示的逆向路径追踪方法虽能强制执行正确的光传输，却对几何精度要求极高，难以鲁棒应用于真实室内场景。本文提出发射感知高斯与路径追踪（EAG-PT），一种利用统一二维高斯表征实现室内场景物理基重建与渲染的方法，面向可编辑的漫反射全局光照。该方法包含三个核心思想：（1）采用二维高斯作为传输友好的几何代理表示室内场景，避免显式网格重建；（2）在重建过程中将发射分量与非发射分量显式分离以支持编辑；（3）通过高效的单次弹射优化与高质量多次弹射路径追踪分别解耦重建与最终渲染。在合成与真实室内场景上的实验表明，相比辐射场重建方法，EAG-PT可生成更自然且物理一致的编辑渲染结果；同时相较于基于网格的逆向路径追踪，该方法能保留更精细的几何细节并避免网格伪影。这些结果凸显了该方法在室内设计、扩展现实内容生成及具身人工智能等领域的应用潜力。