  We introduce a novel neural network-based computational pipeline as a
representation-agnostic slicer for multi-axis 3D printing. This advanced slicer
can work on models with diverse representations and intricate topology. The
approach involves employing neural networks to establish a deformation mapping,
defining a scalar field in the space surrounding an input model. Isosurfaces
are subsequently extracted from this field to generate curved layers for 3D
printing. Creating a differentiable pipeline enables us to optimize the mapping
through loss functions directly defined on the field gradients as the local
printing directions. New loss functions have been introduced to meet the
manufacturing objectives of support-free and strength reinforcement. Our new
computation pipeline relies less on the initial values of the field and can
generate slicing results with significantly improved performance.
