  In this manuscript, inspired by a simpler reformulation of primary sample
space Metropolis light transport, we derive a novel family of general Markov
chain Monte Carlo algorithms called charted Metropolis-Hastings, that
introduces the notion of sampling charts to extend a given sampling domain and
making it easier to sample the desired target distribution and escape from
local maxima through coordinate changes. We further apply the novel algorithms
to light transport simulation, obtaining a new type of algorithm called charted
Metropolis light transport, that can be seen as a bridge between primary sample
space and path space Metropolis light transport. The new algorithms require to
provide only right inverses of the sampling functions, a property that we
believe crucial to make them practical in the context of light transport
simulation. We further propose a method to integrate density estimation into
this framework through a novel scheme that uses it as an independence sampler.
