用滑动盒子投影重建细长3D结构，提升血管和管道细节还原度。
SBP-Net: Learning Thin Structure Reconstruction with Sliding-Box Projections
- 通过滑动盒子生成局部正交深度投影，实现高效2D表征
- 在肺动脉与工业管道数据上，细结构保留效果优于现有方法
- 适合医疗与工业领域中细长物体的高精度三维重建
由于稀疏性、尺度变化和复杂几何特征，重建细长3D结构极具挑战。这类结构广泛存在于血管系统与工业管道系统中。尽管近期神经方法在密集表面重建上表现良好，但难以恢复精细的细长几何形态。本文提出一种基于局部深度投影的重建方法，利用滑动盒子生成局部正交深度投影，以高效且信息丰富的2D形式表示细长结构。该投影由神经网络处理，用于恢复缺失的细结构；随后将局部重建结果融合回3D模型，生成连贯且细节丰富的形状。在肺动脉从CT体积重建以及合成与真实扫描数据中的工业管道恢复实验中，本方法显著提升了对细结构细节的保持能力。
原文摘要 · Abstract (English)
Reconstructing thin 3D structures is challenging due to their sparsity, scale variation, and complex geometry. Such structures arise in a wide range of domains, including medical imaging of vascular systems and industrial pipe systems. While recent neural methods perform well on dense surfaces, they often fail to recover fine thin geometries. We propose a reconstruction approach based on local depth projections, which provide an efficient and informative 2D representation of thin structures. Specifically, we traverse the 3D model with a sliding box to generate local orthographic depth projections, which are processed by a neural network to reconstruct missing thin structures in 2D. The local reconstructions are subsequently fused back into the 3D model to produce a coherent and detailed shape. Experiments on pulmonary artery reconstruction from CT volumes and industrial pipeline recovery from synthetic and real scans demonstrate improved preservation of fine structural details over existing methods.
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