预积分神经发射器的实时渲染

中文摘要 AI 辅助

在实时渲染中，来自具有遮挡外壳、高多边形发射网格、空间变化发射或可变形组件的非平凡发射器的直接照明一直是一个持续存在的瓶颈。当前的实时解决方案要么依赖简化的解析表示，要么退回到运行时采样。解析方法仅限于简单的发射器几何形状，纯基于采样的估计器需要高采样预算才能达到无噪声，而代理表示仍然需要在发射器周围对出射辐射进行运行时积分。我们提出了神经发射场（NEF），这是一种通过在发射器周围的体积中预计算照明来消除运行时积分的表示。该神经场由位置、法线、视角方向和材质参数参数化。利用双头漫反射/光泽架构，它能在每个着色点通过单次网络评估产生无噪声的无遮挡直接照明。由于训练在发射器的局部坐标系中进行，训练好的NEF可作为一种便携式照明资产，在刚性变换下可跨场景重用。内部相互反射、自遮挡、空间变化发射和变形都被吸收到学习到的表示中，且零额外运行时成本。

英文摘要

Direct illumination from non-trivial emitters with occluding housing, high-polygon emissive meshes, spatially varying emission, or deformable assemblies is a persistent bottleneck in real-time rendering. Current real-time solutions either rely on simplified analytical representations or fall back to runtime sampling. Analytical methods are restricted to simple emitter geometry, pure sampling-based estimators need high sampling budgets to be noise-free, and proxy representations still require runtime integration over outgoing radiance around the emitter. We present Neural Emission Fields (NEF), a representation that eliminates runtime integration by precomputing the illumination in the volume around the emitter. This neural field is parameterized by position, normal, view direction, and material parameters. Using a two-headed diffuse/glossy architecture, it yields noise-free unoccluded direct illumination from a single network evaluation per shading point. Because training is performed in the emitter's local frame, a trained NEF acts as a portable lighting asset reusable across scenes under rigid transforms. Internal interreflections, self-occlusion, spatially varying emission, and deformations are absorbed into the learned representation at zero additional runtime cost.

发表机构

• Graz University of Technology（格拉茨工业大学）
• Huawei Technologies（华为技术有限公司）

机构由 AI 辅助整理，请以论文原文为准。