  In this paper we present a method using deep learning to compute
parametrizations for B-spline curve approximation. Existing methods consider
the computation of parametric values and a knot vector as separate problems. We
propose to train interdependent deep neural networks to predict parametric
values and knots. We show that it is possible to include B-spline curve
approximation directly into the neural network architecture. The resulting
parametrizations yield tight approximations and are able to outperform
state-of-the-art methods.
