In computed tomography, the approximation quality of a scan of a physical object is typically limited by the acquisition modalities, especially the hardware including X-ray detectors. To improve upon this, we experiment with a three-dimensional subdivision scheme to increase the resolution of the reconstructed voxel data. Subdivision schemes are often used to refine two-dimensional manifolds (mostly meshes) leading to smoother surfaces. In this work, we apply a refinement scheme to three-dimensional data first, and only then, start the surface extraction process. Thus, the main subject of this work lies not on subdivision surfaces, but rather on subdivision volumes. In the volumetric case, each subdivision iteration consumes eight times more storage space than the previous one. Hence, we restrict ourselves to a single subdivision iteration. We evaluate the quality of the produced subdivision volumes using synthetic and industrial data. Furthermore, we consider manufacturing errors in the original and in the subdivision volumes, extract their surfaces, and compare the resulting meshes in critical regions. Observations show that our specific choice of a subdivision scheme produces smoothly interpolated data while also preserving edges.

翻译：在计算机断层扫描中，物理对象扫描的近似质量通常受限于采集方式，特别是包括X射线探测器在内的硬件设备。为改善这一状况，我们尝试采用三维细分方案来提高重建体素数据的分辨率。细分方案常用于细化二维流形（主要是网格），从而获得更光滑的表面。本研究首先对三维数据应用细化方案，随后再进行表面提取过程。因此，本工作主要研究对象并非细分曲面，而是细分体。在体数据情况下，每次细分迭代所需的存储空间是前一次的八倍，故我们仅执行单次细分迭代。我们使用合成数据与工业数据对所生成的细分体质量进行评估。此外，我们考虑原始体与细分体中的制造误差，提取其表面，并在关键区域比较生成的网格。实验结果表明，我们特定选择的细分方案能在保留边缘的同时生成光滑插值数据。