Neural Path Sampling for Rendering Pure Specular Light Transport

Multi-bounce, pure specular light paths produce complex lighting effects, such as caustics and sparkle highlights, which are challenging to render due to their sparse and diverse nature. We introduce a learning-based method for the efficient rendering of pure specular light transport. The key idea is training a neural network to model the distribution of all specular light paths between pairs of endpoints for one specular object. To achieve this, for each object, our method models the distribution of sparse and diverse specular light paths between two endpoints using smooth 2D maps of ray directions from one endpoint and represents these maps with a 2D convolutional network. We design a training scheme to efficiently sample specular light paths from the scene and train the network. Once trained, our method predicts specular light paths for a given pair of endpoints using the network and employs root-finding-based algorithms for rendering the specular light transport. Experimental results demonstrate that our method generates high-quality results, supports dynamic lighting and moving objects within the scene, and significantly enhances the rendering speed of existing techniques. We introduce a learning-based method for the efficient rendering of pure specular light transport by training a neural network to model the distribution of all specular light paths between pairs of endpoints for one specular object.image