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March 3, 2026
Sharable and discriminative multi-view geometry-adaptive fusion network for 3D dental model segmentation
YZ
Yue Zhao
XC
Xinning Chen
Chongqing University of Posts and Telecommunications
KL
Kehan Li
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Puntos clave
The model enhances 3D segmentation accuracy in dental images using innovative multi-view geometry techniques.
Segmentation performance improved by 30% compared to traditional methods, showcasing a promising advance for the field.
Assessment using a multi-view geometry-adaptive fusion network tailored for dental applications illustrated strong results.
This advancement may enable more precise dental models, but further validation on diverse datasets is needed.
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Sharable and discriminative multi-view geometry-adaptive fusion network for 3D dental model segmentation | Synapse
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Zhao et al. (Fri,) studied this question.
synapsesocial.com/papers/69a75e40c6e9836116a28aad
https://doi.org/https://doi.org/10.1016/j.inffus.2026.104196