We introduce Neural Radiosity, an algorithm to solve the rendering equation by minimizing the norm of its residual similar as in traditional radiosity techniques. Traditional basis functions used in radiosity techniques, such as piecewise polynomials or meshless basis functions are typically limited to representing isotropic scattering from diffuse surfaces. Instead, we propose to leverage neural networks to represent the full four-dimensional radiance distribution, directly optimizing network parameters to minimize the norm of the residual. Our approach decouples solving the rendering equation from rendering (perspective) images similar as in traditional radiosity techniques, and allows us to efficiently synthesize arbitrary views of a scene. In addition, we propose a network architecture using geometric learnable features that improves convergence of our solver compared to previous techniques. Our approach leads to an algorithm that is simple to implement, and we demonstrate its effectiveness on a variety of scenes with non-diffuse surfaces.
翻译:我们引入神经辐射, 这是一种算法,通过尽量减少其残留物与传统放射技术相似的规范来解析等值。 在射电技术中使用的传统基础功能,例如小巧的多元光学或无网基功能,通常仅限于代表分散表面的异向散射。 相反, 我们提议利用神经网络代表四维光谱的完整分布, 直接优化网络参数以最大限度地减少残余物的规范。 我们的方法解析了从像传统放射技术那样的图像(透视)成像中解析的方程, 并使我们能够有效地合成一个场景的任意观点。 此外, 我们提议一个网络结构, 使用几何学特征来改进我们解答器与以往技术的趋同。 我们的方法引出一个简单执行的算法, 我们用非光学表面的各种场面展示其有效性。