用物理渲染原理增强神经辐射场，同时还原场景材质与光照。
PBR-NeRF: Inverse Rendering with Physics-Based Neural Fields
- 基于物理渲染理论构建新损失项，约束材质与光照估计。
- 在保持高质量新视角合成的同时，实现最优材质重建效果。
- 适合需要精确材质建模的3D重建与逆向渲染研究者使用。
本文针对3D重建中的病态逆向渲染问题，提出基于物理渲染（PBR）理论的神经辐射场方法PBR-NeRF。现有大多数NeRF与3D高斯泼溅方法仅估计视图相关的外观，未建模场景材质与光照。PBR-NeRF通过引入两个新颖的物理启发先验，有效约束逆向渲染过程，实现几何、材质与光照的联合估计。这些先验被严格形式化为直观的损失项，在不牺牲新视角合成质量的前提下，达到当前最佳的材质重建性能。该方法可轻松集成至其他需材质估计的逆向渲染与3D重建框架中。结果表明，扩展神经渲染以完整建模场景属性至关重要。代码已公开于https://github.com/s3anwu/pbrnerf。
原文摘要 · Abstract (English)
We tackle the ill-posed inverse rendering problem in 3D reconstruction with a Neural Radiance Field (NeRF) approach informed by Physics-Based Rendering (PBR) theory, named PBR-NeRF. Our method addresses a key limitation in most NeRF and 3D Gaussian Splatting approaches: they estimate view-dependent appearance without modeling scene materials and illumination. To address this limitation, we present an inverse rendering (IR) model capable of jointly estimating scene geometry, materials, and illumination. Our model builds upon recent NeRF-based IR approaches, but crucially introduces two novel physics-based priors that better constrain the IR estimation. Our priors are rigorously formulated as intuitive loss terms and achieve state-of-the-art material estimation without compromising novel view synthesis quality. Our method is easily adaptable to other inverse rendering and 3D reconstruction frameworks that require material estimation. We demonstrate the importance of extending current neural rendering approaches to fully model scene properties beyond geometry and view-dependent appearance. Code is publicly available at https://github.com/s3anwu/pbrnerf
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