Monte Carlo rendering of translucent objects with heterogeneous scattering properties is often expensive both in terms of memory and computation. If we do path tracing and use a high dynamic range lighting environment, the rendering becomes computationally heavy. We propose a compact and efficient neural method for representing and rendering the appearance of heterogeneous translucent objects. The neural representation function resembles a bidirectional scattering-surface reflectance distribution function (BSSRDF). However, conventional BSSRDF models assume a planar half-space medium and only surface variation of the material, which is often not a good representation of the appearance of real-world objects. Our method represents the BSSRDF of a full object taking its geometry and heterogeneities into account. This is similar to a neural radiance field, but our representation works for an arbitrary distant lighting environment. In a sense, we present a version of neural precomputed radiance transfer that captures all-frequency relighting of heterogeneous translucent objects. We use a multi-layer perceptron (MLP) with skip connections to represent the appearance of an object as a function of spatial position, direction of observation, and direction of incidence. The latter is considered a directional light incident across the entire non-self-shadowed part of the object. We demonstrate the ability of our method to store highly complex materials while having high accuracy when comparing to reference images of the represented object in unseen lighting environments. As compared with path tracing of a heterogeneous light scattering volume behind a refractive interface, our method more easily enables importance sampling of the directions of incidence and can be integrated into existing rendering frameworks while achieving interactive frame rates.

翻译：具有非均匀散射特性的半透明物体的蒙特卡洛渲染往往在内存和计算上都很昂贵。如果采用路径追踪并使用高动态范围光照环境，渲染计算量将变得极其庞大。我们提出了一种紧凑高效的神经方法，用于表示和渲染非均匀半透明物体的外观。该神经表示函数类似于双向散射-表面反射分布函数（BSSRDF）。然而，传统BSSRDF模型假设平面半空间介质且仅考虑材料表面变化，这通常无法很好地表示真实世界物体的外观。我们的方法在考虑物体几何形状和非均匀性的前提下，完整表示其BSSRDF。这与神经辐射场类似，但我们的表示适用于任意远距离光照环境。从某种意义上说，我们提出了一种神经预计算辐射传输的变体，能够捕捉非均匀半透明物体的全频率重光照效果。我们采用带跳跃连接的多层感知机（MLP），将物体外观表示为空间位置、观测方向和入射方向的函数。其中入射方向被视为作用于物体非自遮挡区域的定向光源。实验表明，我们的方法能够存储高度复杂的材质，同时在未见过的光照环境下与参考图像对比时保持高精度。与在折射界面后方追踪非均匀光散射体的路径追踪方法相比，我们的方法更易于实现入射方向的重要性采样，并能集成到现有渲染框架中，同时实现交互式帧率。