Memory-Guided Point Cloud Completion for Dental Reconstruction

• URL: http://arxiv.org/abs/2512.03598v1
• Date: Wed, 03 Dec 2025 09:31:07 GMT
• Title: Memory-Guided Point Cloud Completion for Dental Reconstruction
• Abstract summary: We propose a retrieval-augmented framework for tooth completion that integrates a prototype memory into encoder--decoder pipelines.<n>The memory is optimized end-to-end and self-organizes into reusable tooth-shape prototypes.<n>Our approach provides a simple yet effective way to exploit cross-sample regularities for more accurate and faithful dental point-cloud completion.
• Score: 2.9422243774213075
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Partial dental point clouds often suffer from large missing regions caused by occlusion and limited scanning views, which bias encoder-only global features and force decoders to hallucinate structures. We propose a retrieval-augmented framework for tooth completion that integrates a prototype memory into standard encoder--decoder pipelines. After encoding a partial input into a global descriptor, the model retrieves the nearest manifold prototype from a learnable memory and fuses it with the query feature through confidence-gated weighting before decoding. The memory is optimized end-to-end and self-organizes into reusable tooth-shape prototypes without requiring tooth-position labels, thereby providing structural priors that stabilize missing-region inference and free decoder capacity for detail recovery. The module is plug-and-play and compatible with common completion backbones, while keeping the same training losses. Experiments on a self-processed Teeth3DS benchmark demonstrate consistent improvements in Chamfer Distance, with visualizations showing sharper cusps, ridges, and interproximal transitions. Our approach provides a simple yet effective way to exploit cross-sample regularities for more accurate and faithful dental point-cloud completion.

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