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We propose a computational model for shape, illumination and albedo inferencein a pulsed time-of-flight (TOF) camera. In contrast to TOF cameras based onphase modulation, our camera enables general exposure profiles. This results inadded flexibility and requires novel computational approaches.  To address this challenge we propose a generative probabilistic model thataccurately relates latent imaging conditions to observed camera responses.While principled, realtime inference in the model turns out to be infeasible,and we propose to employ efficient non-parametric regression trees toapproximate the model outputs. As a result we are able to provide, for eachpixel, at video frame rate, estimates and uncertainty for depth, effectivealbedo, and ambient light intensity. These results we present arestate-of-the-art in depth imaging.  The flexibility of our approach allows us to easily enrich our generativemodel. We demonstrate that by extending the original single-path model to atwo-path model, capable of describing some multipath effects. The new model isseamlessly integrated in the system at no additional computational cost.  Our work also addresses the important question of optimal exposure design inpulsed TOF systems. Finally, for benchmark purposes and to obtain realisticempirical priors of multipath and insights into this phenomena, we propose aphysically accurate simulation of multipath phenomena.

Bayesian Time-of-Flight for Realtime Shape, Illumination and Albedo