  This work introduces progressive spatio-temporal filtering, an efficient
method to build all-frequency approximations to the light transport
distribution into a scene by filtering individual samples produced by an
underlying path sampler, using online, iterative algorithms and data-structures
that exploit both the spatial and temporal coherence of the approximated light
field. Unlike previous approaches, the proposed method is both more efficient,
due to its use of an iterative temporal feedback loop that massively improves
convergence to a noise-free approximant, and more flexible, due to its
introduction of a spatio-directional hashing representation that allows to
encode directional variations like those due to glossy reflections. We then
introduce four different methods to employ the resulting approximations to
control the underlying path sampler and/or modify its associated estimator,
greatly reducing its variance and enhancing its robustness to complex lighting
scenarios. The core algorithms are highly scalable and low-overhead, requiring
only minor modifications to an existing path tracer.
