Fast Differentiable Transient Rendering for Non-Line-of-Sight Reconstruction

Research into non-line-of-sight imaging problems has gained momentum in recent years motivated by intriguing prospective applications in e.g. medicine and autonomous driving. While transient image formation is well understood and there exist various reconstruction approaches for non-line-of-sight scenes that combine efficient forward renderers with optimization schemes, those approaches suffer from runtimes in the order of hours even for moderately sized scenes. Furthermore, the ill-posedness of the inverse problem often leads to instabilities in the optimization.Inspired by the latest advances in direct-line-of-sight inverse rendering that have led to stunning results for reconstructing scene geometry and appearance, we present a fast differentiable transient renderer that accelerates the inverse rendering runtime to minutes on consumer hardware, making it possible to apply inverse transient imaging on a wider range of tasks and in more time-critical scenarios. We demonstrate its effectiveness on a series of applications using various datasets and show that it can be used for self-supervised learning.