Consider a set of $N$ points in $n$-dimensional space, i.e. \begin{align*} S = \{x_1, \dots, x_N\} \subset \mathbb R^n. \end{align*} Let $v \in \mathbb R^n$ and consider the image set (not counting multiplicities (!)) $$ P_v S := \{y \; | \;\exists x \in S \text{ such that } \langle v , x \rangle = y \} $$ which can be seen as a projection of the set $S=\{x_1, \dots, x_N\}$.
Consider the problem of reconstructing the set $S$ from projections $P_v S$ where $v \in V \subset \mathbb R^n$.
My question is: Under which conditions on the family of vectors $V$ we can uniquely reconstruct the set $S=\{x_1, \dots, x_N\}$? Clearly it is necessary that $V$ contains a basis of $\mathbb R^n$. But this is not sufficient as one easily sees in simple examples (e.g. $n=2$, $N=3$). Loosely speaking, the set $V$ has to be large enough.