pith:QVBGNOBZ
TSegAgent: Zero-Shot Tooth Segmentation via Geometry-Aware Vision-Language Agents
TSegAgent achieves zero-shot tooth segmentation in 3D dental scans by turning the task into geometry-grounded reasoning with vision-language agents.
arxiv:2603.19684 v3 · 2026-03-20 · cs.CV
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Claims
Experimental results demonstrate that this reasoning-oriented formulation enables accurate and reliable tooth segmentation and identification with low computational and annotation cost, while exhibiting strong generalization across diverse and previously unseen dental scans.
That multi-view visual abstraction combined with geometry-grounded reasoning from general foundation models can reliably infer tooth instances and identities solely from encoded dental anatomy constraints such as arch organization and volumetric relationships, without any task-specific training.
TSegAgent achieves accurate zero-shot tooth segmentation on 3D dental scans via geometry-aware vision-language reasoning without task-specific training.
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Receipt and verification
| First computed | 2026-06-24T01:14:27.087724Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
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curl -sH 'Accept: application/ld+json' https://pith.science/pith/QVBGNOBZUWII24Y4WOHSG7S2UO \
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Canonical record JSON
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