A diffusion model trained on synthetically damaged teeth from public datasets completes crowns with 81.8% IoU and 0.00034 Chamfer distance, and produces real-world restorations with minimal opposing-tooth interference.
Diffusion probabilistic models for 3d point cloud generation
2 Pith papers cite this work. Polarity classification is still indexing.
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Introduces O-Voxel omni-voxel representation and Sparse Compression VAE for structured native 3D latents, enabling efficient training of large flow-matching models that produce higher-quality geometry and materials than prior methods.
citing papers explorer
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From Synthetic Data to Real Restorations: Diffusion Model for Patient-specific Dental Crown Completion
A diffusion model trained on synthetically damaged teeth from public datasets completes crowns with 81.8% IoU and 0.00034 Chamfer distance, and produces real-world restorations with minimal opposing-tooth interference.
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Native and Compact Structured Latents for 3D Generation
Introduces O-Voxel omni-voxel representation and Sparse Compression VAE for structured native 3D latents, enabling efficient training of large flow-matching models that produce higher-quality geometry and materials than prior methods.