Our paper "MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillation" (Kamil Kwarciak, Marek Wodziński) was accepted and presented at SASHIMI, the 11th International Workshop on Simulation and Synthesis in Medical Imaging, held on October 1, 2026 in Strasbourg, France as part of MICCAI 2026.
Point cloud flow matching produces high-quality cranial implant reconstructions, but sampling typically requires many neural function evaluations, which slows down generating multiple candidate implants. MedPCFM-TED introduces Teacher-guided Endpoint Distillation, a one-step distillation framework that trains a student model with teacher-guided endpoint supervision and geometric matching losses, without explicit path straightening. On the SkullFix and SkullBreak benchmarks, it performs best overall on SkullBreak, stays competitive on SkullFix, and achieves the strongest Chamfer distance among compared one-step methods, generating an implant in roughly 0.04 seconds, a substantial speed-up over iterative flow-matching sampling without sacrificing reconstruction quality.