arXiv:2606.07907v1 Announce Type: cross Abstract: 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 improv
This research builds on previous work, demonstrating continuous advancements in refining 3D reconstruction accuracy for specific applications, pushed by ongoing improvements in deep learning techniques.
Improved 3D oral modeling can enhance precision in dental diagnostics, treatment planning, and custom appliance manufacturing, leading to better patient outcomes and more efficient operations.
The refinement of vertex distribution addresses a key limitation in previous 3D reconstruction models, offering more uniform and complete surface coverage crucial for medical applications.
- · Dental tech companies
- · Medical AI developers
- · Patients receiving dental care
- · Traditional oral impression methods
More accurate and reliable 3D models for intraoral applications become widely available.
This leads to accelerated adoption of AI-powered diagnostic and manufacturing tools in dentistry.
The enhanced precision could enable new forms of personalized dental prosthetics and interventions, currently limited by modeling accuracy.
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Read at arXiv cs.AI