论文标题
可区分的神经辐射度
Differentiable Neural Radiosity
论文作者
论文摘要
我们介绍了可微分的神经辐射度，这是一种使用神经网络来表示差分渲染方程的解的新方法。受神经辐射技术的启发，我们最大程度地降低了差分渲染方程的残差的规范，以直接优化我们的网络。该网络能够考虑到差异的全局照明效果，同时保持记忆和时间复杂性在路径长度上恒定，从而可以相对于场景参数输出辐射场连续的，无关的梯度。为了解决逆渲染问题，我们使用网络的预训练实例，该实例代表相对于有限数量的场景参数的差异辐射字段。在我们的实验中，与其他技术（例如自动分化，辐射反向传播和路径重播反向传播）相比，我们利用这一点来实现更快，更准确的收敛性。
We introduce Differentiable Neural Radiosity, a novel method of representing the solution of the differential rendering equation using a neural network. Inspired by neural radiosity techniques, we minimize the norm of the residual of the differential rendering equation to directly optimize our network. The network is capable of outputting continuous, view-independent gradients of the radiance field with respect to scene parameters, taking into account differential global illumination effects while keeping memory and time complexity constant in path length. To solve inverse rendering problems, we use a pre-trained instance of our network that represents the differential radiance field with respect to a limited number of scene parameters. In our experiments, we leverage this to achieve faster and more accurate convergence compared to other techniques such as Automatic Differentiation, Radiative Backpropagation, and Path Replay Backpropagation.
