  In the Gaussian white noise model, we study the estimation of an unknown
multidimensional function $f$ in the uniform norm by using kernel methods. The
performances of procedures are measured by using the maxiset point of view: we
determine the set of functions which are well estimated (at a prescribed rate)
by each procedure. So, in this paper, we determine the maxisets associated to
kernel estimators and to the Lepski procedure for the rate of convergence of
the form $(\log n/n)^{\be/(2\be+d)}$. We characterize the maxisets in terms of
Besov and H\"older spaces of regularity $\beta$.
