Particle methods, also known as Sequential Monte Carlo methods, are a popular set of computational tools used to sample approximately from non-standard probability distri- butions. A variety of convergence results ensure that, under weak assumptions, the distribution of the particles converges to the target probability distribution of interest as the number of particles increases to infinity. Unfortunately it can be difficult to determine practically how large this number needs to be to obtain a reliable approximation. We propose here a procedure which allows us to return exact samples. The proposed algorithm relies on the combination of an original branching variant of particle Markov chain Monte Carlo methods and dominated coupling from the past.