Accurate subsurface scattering solutions require the integration of optical material properties along many complicated light paths. We present a method that learns a simple geometric approximation of random paths in a homogeneous volume of translucent material. The generated representation allows determining the absorption along the path as well as a direct lighting contribution, which is representative of all scattering events along the path. A sequence of conditional variational auto-encoders (CVAEs) is trained to model the statistical distribution of the photon paths inside a spherical region in presence of multiple scattering events. A first CVAE learns to sample the number of scattering events, occurring on a ray path inside the sphere, which effectively determines the probability of the ray being absorbed. Conditioned on this, a second model predicts the exit position and direction of the light particle. Finally, a third model generates a representative sample of photon position and direction along the path, which is used to approximate the contribution of direct illumination due to in-scattering. To accelerate the tracing of the light path through the volumetric medium toward the solid boundary, we employ a sphere-tracing strategy that considers the light absorption and is able to perform statistically accurate next-event estimation. We demonstrate efficient learning using shallow networks of only three layers and no more than 16 nodes. In combination with a GPU shader that evaluates the CVAEs' predictions, performance gains can be demonstrated for a variety of different scenarios. A quality evaluation analyzes the approximation error that is introduced by the data-driven scattering simulation and sheds light on the major sources of error in the accelerated path tracing process.

中文翻译：

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学习多重散射解决方案，用于体积次表面效应的球体追踪

准确的次表面散射解决方案需要沿许多复杂的光路集成光学材料特性。我们提出了一种方法，可以在半透明材料的均匀体积中学习随机路径的简单几何近似。生成的表示允许确定沿路径的吸收以及直接照明贡献，它代表沿路径的所有散射事件。一系列条件变分自动编码器 (CVAE) 被训练以在存在多个散射事件的情况下对球形区域内的光子路径的统计分布进行建模。第一个 CVAE 学习对发生在球体内部光线路径上的散射事件的数量进行采样，这有效地确定了光线被吸收的概率。以此为条件，第二个模型预测光粒子的出口位置和方向。最后，第三个模型生成沿路径的光子位置和方向的代表性样本，用于近似由于内散射引起的直接照明的贡献。为了加速通过体积介质向固体边界追踪光路，我们采用了一种球体追踪策略，该策略考虑了光吸收并能够执行统计上准确的下一事件估计。我们展示了使用只有三层和不超过 16 个节点的浅层网络的高效学习。结合评估 CVAE 预测的 GPU 着色器，可以针对各种不同场景展示性能提升。