  Advances in 3D printing of biocompatible materials make patient-specific
implants increasingly popular. The design of these implants is, however, still
a tedious and largely manual process. Existing approaches to automate implant
generation are mainly based on 3D U-Net architectures on downsampled or
patch-wise data, which can result in a loss of detail or contextual
information. Following the recent success of Diffusion Probabilistic Models, we
propose a novel approach for implant generation based on a combination of 3D
point cloud diffusion models and voxelization networks. Due to the stochastic
sampling process in our diffusion model, we can propose an ensemble of
different implants per defect, from which the physicians can choose the most
suitable one. We evaluate our method on the SkullBreak and SkullFix datasets,
generating high-quality implants and achieving competitive evaluation scores.
