We introduce a single-view reconstruction technique of volumetric fields in which multiple light scattering effects are omnipresent, such as in clouds. We model the unknown distribution of volumetric fields using an unconditional diffusion model trained on a novel benchmark dataset comprising 1,000 synthetically simulated volumetric density fields. The neural diffusion model is trained on the latent codes of a novel, diffusion-friendly, monoplanar representation. The generative model is used to incorporate a tailored parametric diffusion posterior sampling technique into different reconstruction tasks. A physically-based differentiable volume renderer is employed to provide gradients with respect to light transport in the latent space. This stands in contrast to classic NeRF approaches and makes the reconstructions better aligned with observed data. Through various experiments, we demonstrate single-view reconstruction of volumetric clouds at a previously unattainable quality.

本研究针对云等存在多重光散射效应的体积场单视图重建问题，提出了一种新方法。研究中利用在一个包含1000个合成体积密度场的新基准数据集上训练的无条件扩散模型，实现体积场的建模，并通过物理基础的可微体积渲染器优化重建效果。实验表明，本方法在单视图重建体积云的质量上超过了以往的方法。

光传输感知的扩散后验采样用于单视图3D体积重建