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arXiv Machine Learning By Kamil Kwarciak, Marek Wodzinski

MedPCFM-TED: One-Step Point Cloud Flow Matching for Implant Generation via Teacher-Guided Endpoint Distillation

Read the original on arXiv Machine Learning →

The paper introduces MedPCFM‑TED, a one‑step distillation framework that uses teacher‑guided endpoint supervision and geometric matching losses to generate cranial implants from point clouds. It outperforms existing one‑step methods on the SkullBreak benchmark, remains competitive on SkullFix, and achieves a generation time of about 0.04 s per sample. The approach demonstrates that rapid, high‑quality implant generation is possible without multiple neural evaluations during inference.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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