Med-PU: Point Cloud Upsampling for High-Fidelity 3D Medical Shape Reconstruction

Le résumé fourni par la source

High-fidelity 3D anatomical reconstruction is essential for clinical applications, yet converting voxel-based segmentation into smooth and watertight meshes remains challenging due to noise, incompleteness, and shape variability. We propose Med-PU, a knowledge-driven framework that integrates volumetric segmentation with deep point cloud upsampling to reconstruct high-quality anatomical surfaces. Instead of manual landmarking or PCA-based statistical models, Med-PU learns an implicit anatomical prior from large-scale 3D medical shapes, enabling dense and topology-consistent reconstruction from sparse segmentation outputs. Experiments on pelvic CT datasets (Med-ShapePelvic for training and Pelvic1k for validation) show that Med-PU consistently improves geometric accuracy and surface fidelity over state-of-the-art upsampling methods, demonstrating robustness across input densities and potential generalization to other anatomical regions.

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