改进点云分布均匀性，解决口腔3D重建中顶点聚集问题
3D Oral Modelling with Improved Vertex Distribution Using Matching-Based Learning
- 引入匈牙利匹配与斥力损失，优化顶点分布
- 顶点分布更均匀，覆盖区域显著改善
- 适合需要全局一致性的口腔建模应用
在之前的工作中，我们提出了一种基于深度学习的3D口腔重建框架，直接从10张固定角度的口内图像预测显式3D点云坐标，采用MobileNetV2和多头注意力进行多视角特征融合，使用L1损失与切比雪夫距离的组合损失函数。尽管模型达到了77.49%的准确率，但预测顶点往往集中在真实数据的高密度区域，导致其他区域覆盖不足。本文提出一种改进的损失函数，引入带过滤的匈牙利匹配与斥力损失，以实现更均匀的顶点分布。新模型准确率为68.02%，数值低于先前模型，但显著缓解了顶点聚类问题，使预测顶点在重建表面更加均匀分布。
原文摘要 · Abstract (English)
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 uniform vertex distribution across the reconstructed model. The proposed model achieves an accuracy of 68.02%, which is numerically lower than the previous model. However, the vertex clustering issue observed in the prior work is substantially alleviated, with predicted vertices distributed more evenly across the entire reconstructed surface.
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