Precomputed Radiance Transfer (PRT) is widely used for real-time photorealistic effects. PRT disentangles the rendering equation into transfer and lighting, enabling their precomputation. Transfer accounts for the cosine-weighted visibility of points in the scene while lighting for emitted radiance from the environment. Prior art stored precomputed transfer in a tabulated manner, either in vertex or texture space. These values are fetched with interpolation at each point for shading. Vertex space methods require densely tessellated mesh vertices for high quality images. Texture space methods require non-overlapping and area-preserving UV mapping to be available. They also require a high-resolution texture to avoid rendering artifacts. In this paper, we propose a compact transfer representation that is learnt directly on scene geometry points. Specifically, we train a small multi-layer perceptron (MLP) to predict the transfer at sampled surface points. Our approach is most beneficial where inherent mesh storage structure and natural UV mapping are not available, such as Implicit Surfaces as it learns the transfer values directly on the surface. We demonstrate real-time, photorealistic renderings of diffuse and glossy materials on SDF geometries with PRT using our approach.
翻译:PRT 将转换方程式分解成传输和照明, 从而能够进行预先计算。 传输时要记录现场各点的焦线加权可见度, 并同时为环境中排放的亮光点照明。 先前的艺术品储存预先传输, 无论是在顶部还是纹理空间, 都以制表方式进行。 这些数值在每个位置都以内插方式获取阴影。 Vertex 空间方法需要高品质图像的密闭网状网格。 质地空间方法需要不过度拍摄和区域保留UV 映射。 它们也需要高分辨率的纹理来避免绘制人工制品。 在本文中, 我们提出一个在屏幕几何测距点直接学习的缩缩缩缩缩图。 具体地, 我们训练一个小型多层的渗透器( MLP ) 来预测在采样地表点的转移情况。 我们的方法最有益的做法是, 内在的网格存储结构和天然的UV 映射图, 例如, 我们用真实的地平面的图像演示, 我们用地平面的流图来展示。