  We present a novel implicit representation -- neural halfspace representation
(NH-Rep), to convert manifold B-Rep solids to implicit representations. NH-Rep
is a Boolean tree built on a set of implicit functions represented by the
neural network, and the composite Boolean function is capable of representing
solid geometry while preserving sharp features. We propose an efficient
algorithm to extract the Boolean tree from a manifold B-Rep solid and devise a
neural network-based optimization approach to compute the implicit functions.
We demonstrate the high quality offered by our conversion algorithm on ten
thousand manifold B-Rep CAD models that contain various curved patches
including NURBS, and the superiority of our learning approach over other
representative implicit conversion algorithms in terms of surface
reconstruction, sharp feature preservation, signed distance field
approximation, and robustness to various surface geometry, as well as a set of
applications supported by NH-Rep.
