可微神经辐射度

摘要：

我们介绍了微分神经辐射度，一种新的方法表示的差分渲染方程的解决方案，使用神经网络。受神经辐射度技术的启发，我们最小化差分渲染方程残差的范数，以直接优化我们的网络。该网络能够输出连续的，独立于视图的梯度辐射场的场景参数，考虑到差分全局光照的影响，同时保持内存和时间复杂度恒定的路径长度。为了解决逆渲染问题，我们使用我们的网络的一个预先训练的实例，该实例表示相对于有限数量的场景参数的差分辐射场。在我们的实验中，我们利用这一点来实现更快，更准确的收敛相比，其他技术，如自动微分，辐射反向传播，路径重放反向传播。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。