3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning
In our previous work, a deep learning-based framework for 3D intraoral reconstruction was proposed. The model directly predicts explicit 3D point cloud coordinates from ten fixed-angle intraoral images, employing MobileNetV2 and Multi-head Attention for multi-view feature fusion, with a combined L1 Loss and Chamfer Distance as the loss function. Although the model achieved an accuracy of 77.49%, predicted vertices tended to concentrate in high-density regions of the ground truth, leaving other regions largely uncovered. In this paper, an improved loss function is proposed to address this limitation. Hungarian matching with filtering and Repulsion Loss are introduced to enforce more unifor...
| Authors | Jihun Cho, Soo-Yeon Jeong, Eun-Jeong Bae et al. |
|---|---|
| Primary category | cs.CV |
| Published | 6 June 2026 |
| Last updated at source | 24 September 2026 |
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