  Triangulation of a three-dimensional point from at least two noisy 2-D images
can be formulated as a quadratically constrained quadratic program. We propose
an algorithm to extract candidate solutions to this problem from its
semidefinite programming relaxations. We then describe a sufficient condition
and a polynomial time test for certifying when such a solution is optimal. This
test has no false positives. Experiments indicate that false negatives are
rare, and the algorithm has excellent performance in practice. We explain this
phenomenon in terms of the geometry of the triangulation problem.
