  Estimating the free energy in molecular simulation requires, implicitly or
explicitly, counting how many times the system is observed in a finite region.
If the simulation is biased by an external potential, the weight of the
configurations within the region can vary significantly, and this can make the
estimate numerically unstable. We introduce an approach to estimate the free
energy as a simultaneous function of several collective variables starting from
data generated in a statically-biased simulation. The approach exploits the
property of a free energy estimator recently introduced by us which provides by
construction the estimate in a region of infinitely small size. We show that
this property allows removing the effect of the external bias in a simple and
rigorous manner. The approach is validated on model systems for which the free
energy is known analytically and on a small peptide for which the ground truth
free energy is estimated in an independent unbiased run. In both cases the free
energy obtained with our approach is an unbiased estimator of the ground-truth
free energy, with an error whose magnitude is also predicted by the model.
