用切片轮廓重建3D薄结构，避免失真和过度平滑。
CrossSDF: 3D Reconstruction of Thin Structures From Cross-Sections
- 基于2D轮廓生成3D符号距离场，训练时感知切片几何信息。
- 在真实医学数据上重建细小血管结构，精度显著优于现有方法。
- 适合需要高保真3D建模的医疗影像与精密制造场景。
从平面切片重构复杂结构是一项挑战性任务，广泛应用于医学成像、制造和地形测绘。传统点云重建方法常因切片间数据稀疏而失效，现有定制方法难以准确重建薄几何结构并保持拓扑连续性。这在CT和MRI扫描中尤为关键，因存在细小血管结构。本文提出CrossSDF，一种从2D轮廓生成的符号距离中提取3D符号距离场的新方法。通过设计针对已知2D切片内几何的损失函数，使神经SDF训练具备轮廓感知能力。实验表明，该方法在重建薄结构方面显著优于现有方法，生成高精度3D模型，无插值伪影或过平滑问题。
原文摘要 · Abstract (English)
Reconstructing complex structures from planar cross-sections is a challenging problem, with wide-reaching applications in medical imaging, manufacturing, and topography. Out-of-the-box point cloud reconstruction methods can often fail due to the data sparsity between slicing planes, while current bespoke methods struggle to reconstruct thin geometric structures and preserve topological continuity. This is important for medical applications where thin vessel structures are present in CT and MRI scans. This paper introduces CrossSDF, a novel approach for extracting a 3D signed distance field from 2D signed distances generated from planar contours. Our approach makes the training of neural SDFs contour-aware by using losses designed for the case where geometry is known within 2D slices. Our results demonstrate a significant improvement over existing methods, effectively reconstructing thin structures and producing accurate 3D models without the interpolation artifacts or over-smoothing of prior approaches.
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