Seeking tighter relaxations of combinatorial optimization problems, semidefinite programming is a generalization of linear programming that offers better bounds and is still polynomially solvable. Yet, in practice, a semidefinite program is still significantly harder to solve than a similar-size Linear Program (LP). It is well-known that a semidefinite program can be written as an LP with infinitely-many cuts that could be solved by repeated separation in a Cutting-Planes scheme; this approach is likely to end up in failure. We proposed in [Projective Cutting-Planes, Daniel Porumbel, Siam Journal on Optimization, 2020] the Projective Cutting-Planes method that upgrades t he well-known separation sub-problem to the projection sub-problem: given a feasible $y$ inside a polytope $P$ and a direction $d$, find the maximum $t^∗$ so that $y+t^∗\cdot d \in P$. Using this new sub-problem, one can generate a sequence of both inner and outer solutions that converge to the optimum over $P$. This paper shows that the projection sub-problem can be solved very efficiently in a semidefinite programming context, enabling the resulting method to compete very well with state-of-the-art semidefinite optimization software (refined over decades). Results suggest it may the fastest method for matrix sizes larger than 2000$\times$2000.