  We present an in-depth analysis of the sources of variance in
state-of-the-art unbiased volumetric transmittance estimators, and propose
several new methods for improving their efficiency. These combine to produce a
single estimator that is universally optimal relative to prior work, with up to
several orders of magnitude lower variance at the same cost, and has zero
variance for any ray with non-varying extinction. We first reduce the variance
of truncated power-series estimators using a novel efficient application of
U-statistics. We then greatly reduce the average expansion order of the power
series and redistribute density evaluations to filter the optical depth
estimates with an equidistant sampling comb. Combined with the use of an online
control variate built from a sampled mean density estimate, the resulting
estimator effectively performs ray marching most of the time while using
rarely-sampled higher order terms to correct the bias.
