  Reconstruction of geometry based on different input modes, such as images or
point clouds, has been instrumental in the development of computer aided design
and computer graphics. Optimal implementations of these applications have
traditionally involved the use of spline-based representations at their core.
Most such methods attempt to solve optimization problems that minimize an
output-target mismatch. However, these optimization techniques require an
initialization that is close enough, as they are local methods by nature. We
propose a deep learning architecture that adapts to perform spline fitting
tasks accordingly, providing complementary results to the aforementioned
traditional methods. We showcase the performance of our approach, by
reconstructing spline curves and surfaces based on input images or point
clouds.
