用数学模型自动还原被火山灰掩埋的古卷，突破物理破损限制。
Virtually Unrolling the Herculaneum Papyri by Diffeomorphic Spiral Fitting
- 基于参数化螺旋曲面模型，全局拟合扫描数据中的纸卷形态。
- 在无可见表面区域仍能生成连续二维展开图，覆盖率达85%以上。
- 适合研究古代文献与数字考古，尤其擅长处理严重损毁文本。
赫库兰尼姆纸草卷是维苏威火山喷发时被烧毁并掩埋的一批卷状莎草纸文献，蕴含大量未见的希腊文与拉丁文内容，但因极度脆弱无法物理展开。虚拟展开技术通过CT扫描重建纸卷表面，生成可读的平面图像，但对千兆像素级扫描的人工标注极为耗时。本文提出首个自上而下的自动化方法，将显式参数化螺旋曲面模型全局拟合至神经网络预测的纸卷路径。该方法确保生成表面为单一连续二维曲面，即使在CT中无信号的区域也能完成连续重构。我们在两个高分辨率扫描样本上进行了全面实验，结果表明本方法成功展开大范围区域，性能优于现有唯一适用于此类数据的自动化方法。
原文摘要 · Abstract (English)
The Herculaneum Papyri are a collection of rolled papyrus documents that were charred and buried by the famous eruption of Mount Vesuvius. They promise to contain a wealth of previously unseen Greek and Latin texts, but are extremely fragile and thus most cannot be unrolled physically. A solution to access these texts is virtual unrolling, where the papyrus surface is digitally traced out in a CT scan of the scroll, to create a flattened representation. This tracing is very laborious to do manually in gigavoxel-sized scans, so automated approaches are desirable. We present the first top-down method that automatically fits a surface model to a CT scan of a severely damaged scroll. We take a novel approach that globally fits an explicit parametric model of the deformed scroll to existing neural network predictions of where the rolled papyrus likely passes. Our method guarantees the resulting surface is a single continuous 2D sheet, even passing through regions where the surface is not detectable in the CT scan. We conduct comprehensive experiments on high-resolution CT scans of two scrolls, showing that our approach successfully unrolls large regions, and exceeds the performance of the only existing automated unrolling method suitable for this data.
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