Autonomous path planning for interaction with complex 3D shapes is a critical task in a variety of industrial processes such as spray painting, polishing, and welding. In such scenarios, a robot is typically required to generate multiple end-effector pose paths matching the surface geometry of the input objects. Recent work demonstrates the feasibility of generating path segments for objects with complex shapes via 3D deep learning techniques leveraging expert demonstrations. In this context, a relevant open problem is the combination of such segments into a viable set of long-horizon paths, as generated segments are neither assigned to different paths, nor ordered within each path.