  This paper presents a learning-based method to solve the traditional
parameterization and knot placement problems in B-spline approximation.
Different from conventional heuristic methods or recent AI-based methods, the
proposed method does not assume ordered or fixed-size data points as input.
There is also no need for manually setting the number of knots. It casts the
parameterization and knot placement problems as a sequence-to-sequence
translation problem, a generative process automatically determining the number
of knots, their placement, parameter values, and their ordering. Once trained,
SplineGen demonstrates a notable improvement over existing methods, with a one
to two orders of magnitude increase in approximation accuracy on test data.
