通过对体素数据应用细分实现更平滑的表面
Towards smoother surfaces by applying subdivision to voxel data
摘要：
在计算机断层摄影中，物理对象的扫描的近似质量通常受到采集模态的限制，特别是包括X射线检测器的硬件。为了改善这一点，我们实验了一个三维细分方案，以增加重建体素数据的分辨率。细分方案通常用于细化二维流形（主要是网格），从而获得更光滑的表面。在这项工作中，我们首先将一个细化方案应用于三维数据，然后才开始表面提取过程。因此，这项工作的主要课题不在于细分曲面，而是细分体积。在体积的情况下，每次细分迭代消耗的存储空间是前一次的八倍。因此，我们将自己限制为单个细分迭代。我们使用合成数据和工业数据来评估所产生的细分体积的质量。此外，我们考虑在原始和细分体积中的制造误差，提取它们的表面，并在关键区域中比较所得到的网格。观察表明，我们的细分方案的具体选择产生平滑插值数据，同时也保留边缘。
英文摘要：
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.
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