通过神经辐射传输实现体积逆渲染
Volumetric Inverse Rendering via Neural Radiative Transfer
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中文总结 AI 辅助
研究体积逆渲染问题，提出结合物理光传输与神经优化的方法，将介质光学属性和光场表示为神经场并联合优化，可从多视图图像重建相关参数，还能支持学习参与介质生成模型。
中文摘要 AI 辅助
体积逆渲染旨在从图像中恢复参与介质的光学属性。现有方法要么依赖需要大量算法工作的可微随机光传输模拟，要么使用无法捕捉全局照明的简化模型。我们提出一种将物理完整的光传输与通用神经优化相结合的公式。介质的光学属性和完整光场表示为神经场，并通过联合优化过程估计。通过从局部微分形式的辐射传输方程导出的残差目标来强制实现全局照明，并辅以沿主视射线的体绘制项以减轻低频偏差。我们展示了从多视图图像重建空间变化、颜色分辨的散射、吸收和相位函数参数。除了重建，同一框架还支持学习具有全局照明下物理光学属性的参与介质生成模型。
英文摘要
Volumetric inverse rendering seeks to recover the optical properties of participating media from images. Existing approaches either rely on differentiable stochastic light transport simulation, which require substantial algorithmic effort, or use simplified models that fail to capture global illumination. We propose a formulation that reconciles physically complete light transport with general-purpose neural optimization. The optical properties of the medium and the full light field are represented as neural fields and estimated through a joint optimization process. Global illumination is enforced via a residual objective derived from the Radiative Transfer Equation in local differential form, complemented by a volume rendering term along primary viewing rays to mitigate \rev{low-frequency} bias. We demonstrate reconstruction of spatially varying, color-resolved scattering, absorption, and phase function parameters from multi-view images. Beyond reconstruction, the same framework supports learning generative models of participating media with physical optical properties under global illumination.
