NeuralBoneReg is a self-supervised instance-specific method using neural UDF and MLP-based point cloud registration that matches supervised SOTA accuracy on CT-US and CT-RGB-D bone datasets without inter-subject training data.
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Markerless multi-camera head tracking achieves 2.32 mm and 2.01° median accuracy versus marker-based systems in 50 subjects, sufficient for transcranial magnetic stimulation.
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NeuralBoneReg: An Instance-Specific Label-Free Point Cloud-Based Method for Multi-Modal Bone Surface Registration
NeuralBoneReg is a self-supervised instance-specific method using neural UDF and MLP-based point cloud registration that matches supervised SOTA accuracy on CT-US and CT-RGB-D bone datasets without inter-subject training data.
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Markerless Head Tracking for Accurate and Accessible Neuronavigation
Markerless multi-camera head tracking achieves 2.32 mm and 2.01° median accuracy versus marker-based systems in 50 subjects, sufficient for transcranial magnetic stimulation.