EAG-PT：考虑发射的高斯和路径追踪用于漫反射室内场景重建与编辑
EAG-PT: Emission-Aware Gaussians and Path Tracing for Diffuse Indoor Scene Reconstruction and Editing
AI总结：
EAG-PT通过统一的2D高斯表示实现室内场景的物理重建与渲染，支持可编辑的漫反射全局光照，结合高效单次反弹优化和高质量多次反弹路径追踪，优于现有方法。
摘要：
近期基于辐射场的重建方法，如NeRF和3DGS，在室内场景中实现高视觉保真度，但因烘焙照明和缺乏显式光传输而难以进行场景编辑。相比之下，基于网格表示的逆路径追踪方法虽然能保证正确的光传输，但需要高精度几何，难以稳健应用于真实室内场景。我们提出Emission-Aware Gaussians and Path Tracing (EAG-PT)，一种使用统一的2D高斯表示进行物理重建和渲染的方法，旨在实现可编辑的漫反射全局光照。我们的方法包含三个关键思想：(1) 使用2D高斯表示作为传输友好的几何代理，避免显式网格重建；(2) 在重建过程中显式分离发射和非发射成分以支持编辑；(3) 通过使用高效的单次反弹优化和高质量的多次反弹路径追踪来分离重建和最终渲染。在合成和真实室内场景上的实验表明，EAG-PT产生的编辑渲染比辐射场重建更自然且物理上一致，同时保持更精细的几何细节并避免网格引起的伪影，相比基于网格的逆路径追踪方法具有优势。这些结果突显了我们的方法在室内设计、XR内容创作和具身AI等应用中的潜力。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
