通过动态系数分解，让神经渲染更清晰地表现金属反光和高光。
CoDe-NeRF: Neural Rendering via Dynamic Coefficient Decomposition
- 将材质与光照动态解耦，用静态基底加视点相关系数建模外观
- 在多个挑战性数据集上，反光和高光更锐利真实，细节更丰富
- 适合需要高保真材质渲染的场景重建与视觉生成任务
神经辐射场（NeRF）在新视角合成中表现出色，但在复杂镜面反射和高光建模方面仍面临挑战。现有方法因光照与材质属性纠缠导致反光模糊，或依赖物理逆渲染时出现优化不稳定问题。本文提出基于动态系数分解的神经渲染框架，将复杂外观分解为共享的静态神经基底（编码固有材质属性）与由视点和光照条件驱动的动态系数。动态辐射积分器融合二者生成最终辐射值。在多个挑战性基准测试中，该方法相比现有技术显著提升了镜面高光的锐度与真实感。我们相信此解耦范式为神经场景表示中的复杂外观建模提供了灵活有效的方向。
原文摘要 · Abstract (English)
Neural Radiance Fields (NeRF) have shown impressive performance in novel view synthesis, but challenges remain in rendering scenes with complex specular reflections and highlights. Existing approaches may produce blurry reflections due to entanglement between lighting and material properties, or encounter optimization instability when relying on physically-based inverse rendering. In this work, we present a neural rendering framework based on dynamic coefficient decomposition, aiming to improve the modeling of view-dependent appearance. Our approach decomposes complex appearance into a shared, static neural basis that encodes intrinsic material properties, and a set of dynamic coefficients generated by a Coefficient Network conditioned on view and illumination. A Dynamic Radiance Integrator then combines these components to synthesize the final radiance. Experimental results on several challenging benchmarks suggest that our method can produce sharper and more realistic specular highlights compared to existing techniques. We hope that this decomposition paradigm can provide a flexible and effective direction for modeling complex appearance in neural scene representations.
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