A public benchmark dataset and competition results for 3D dental landmark detection from intraoral scans, with the top team reaching 0.91 rank score using a stratified transformer and DBSCAN.
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4 Pith papers cite this work. Polarity classification is still indexing.
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RobOralScan applies RL with geometric memory and progressive tooth-wise rewards to achieve 92.58% average coverage and 0.00838 Chamfer distance in robotic intraoral scanning.
Equivariant mesh networks with anatomical priors and augmented message passing deliver stable segmentation across edge, vertex, and face supervision while resisting geometric perturbations.
A quantized nnUNet trained with tooth-count, adjacency, and cavity penalties preserves dental topology at 8-bit precision while keeping Dice close to full-precision.
citing papers explorer
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Detecting Dental Landmarks from Intraoral 3D Scans: the 3DTeethLand challenge
A public benchmark dataset and competition results for 3D dental landmark detection from intraoral scans, with the top team reaching 0.91 rank score using a stratified transformer and DBSCAN.
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RobOralScan: Learning Active Intraoral Scanning for Robotic Dental Reconstruction
RobOralScan applies RL with geometric memory and progressive tooth-wise rewards to achieve 92.58% average coverage and 0.00838 Chamfer distance in robotic intraoral scanning.
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Augmented Equivariant Mesh Networks for Anatomical Segmentation
Equivariant mesh networks with anatomical priors and augmented message passing deliver stable segmentation across edge, vertex, and face supervision while resisting geometric perturbations.
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Topology-Constrained Quantized nnUNet for Efficient and Anatomically Accurate 3D Tooth Segmentation
A quantized nnUNet trained with tooth-count, adjacency, and cavity penalties preserves dental topology at 8-bit precision while keeping Dice close to full-precision.