标题： 张量IS：迈向用于Perlin分布异质介质的前向张量逆地下散射 显示英文标题

标题： TensoIS: A Step Towards Feed-Forward Tensorial Inverse Subsurface Scattering for Perlin Distributed Heterogeneous Media

摘要： 从图像中估计异质介质的散射参数是一个严重约束不足且具有挑战性的问题。 现有的大多数方法通过分析-合成方法对BSSRDF进行建模，近似复杂的路径积分，或者使用可微体积渲染技术来考虑异质性。 然而，只有少数研究将基于学习的方法应用于估计次表面散射参数，但它们假设介质是均匀的。 有趣的是，据我们所知，目前还没有特定的分布能够显式地对现实世界中的异质散射参数进行建模。 值得注意的是，程序噪声模型如Perlin和分形 Perlin噪声在表示自然、有机和无机表面的复杂异质性方面非常有效。 利用这一点，我们首先创建了 HeteroSynth，一个包含使用分形 Perlin噪声建模散射参数的异质介质的逼真图像的合成数据集。 此外，我们提出了张量逆散射（TensoIS），一种基于学习的前馈框架，用于从稀疏多视角图像观测中估计这些Perlin分布的异质散射参数。 与直接预测3D散射参数体积不同，TensoIS使用可学习的低秩张量组件来表示散射体积。 我们在HeteroSynth测试集上未见过的异质变化、从开源真实体积模拟中获得的烟雾和云几何形状以及一些真实世界样本上评估TensoIS，以确立其在逆散射中的有效性。 总体而言，本研究是在文献中缺乏任何此类明确定义分布的情况下，尝试探索Perlin噪声分布，以期以前馈方式对现实世界的异质散射进行建模。 显示英文摘要

摘要： Estimating scattering parameters of heterogeneous media from images is a severely under-constrained and challenging problem. Most of the existing approaches model BSSRDF either through an analysis-by-synthesis approach, approximating complex path integrals, or using differentiable volume rendering techniques to account for heterogeneity. However, only a few studies have applied learning-based methods to estimate subsurface scattering parameters, but they assume homogeneous media. Interestingly, no specific distribution is known to us that can explicitly model the heterogeneous scattering parameters in the real world. Notably, procedural noise models such as Perlin and Fractal Perlin noise have been effective in representing intricate heterogeneities of natural, organic, and inorganic surfaces. Leveraging this, we first create HeteroSynth, a synthetic dataset comprising photorealistic images of heterogeneous media whose scattering parameters are modeled using Fractal Perlin noise. Furthermore, we propose Tensorial Inverse Scattering (TensoIS), a learning-based feed-forward framework to estimate these Perlin-distributed heterogeneous scattering parameters from sparse multi-view image observations. Instead of directly predicting the 3D scattering parameter volume, TensoIS uses learnable low-rank tensor components to represent the scattering volume. We evaluate TensoIS on unseen heterogeneous variations over shapes from the HeteroSynth test set, smoke and cloud geometries obtained from open-source realistic volumetric simulations, and some real-world samples to establish its effectiveness for inverse scattering. Overall, this study is an attempt to explore Perlin noise distribution, given the lack of any such well-defined distribution in literature, to potentially model real-world heterogeneous scattering in a feed-forward manner.