  We use Stein's method to establish the rates of normal approximation in terms
of the total variation distance for a large class of sums of score functions of
marked Poisson point processes on $\mathbb{R}^d$. As in the study under the
weaker Kolmogorov distance, the score functions are assumed to satisfy
stabilizing and moment conditions. At the cost of an additional non-singularity
condition for score functions, we show that the rates are in line with those
under the Kolmogorov distance. We demonstrate the use of the theorems in four
applications: Voronoi tessellation, $k$-nearest neighbours, timber volume and
maximal layers.
