  Time-resolved illumination provides rich spatio-temporal information for
applications such as accurate depth sensing or hidden geometry reconstruction,
becoming a useful asset for prototyping and as input for data-driven
approaches. However, time-resolved illumination measurements are
high-dimensional and have a low signal-to-noise ratio, hampering their
applicability in real scenarios. We propose a novel method to compactly
represent time-resolved illumination using mixtures of exponentially-modified
Gaussians that are robust to noise and preserve structural information. Our
method yields representations two orders of magnitude smaller than discretized
data, providing consistent results in applications such as hidden scene
reconstruction and depth estimation, and quantitative improvements over
previous approaches.
