ShreddingNet：面向多源碎纸手稿的由粗到细复原方法
ShreddingNet: Coarse-to-Fine Restoration for Multi-Source Shredded Manuscripts
把「多源碎片拼接」形式化为先按来源聚类、再在候选集内做精细匹配的两阶段流程，其「错误匹配很少跨源」的假设可直接迁移到图像拼接、拼图复原、碎片化文档重建等需要鲁棒特征匹配与离群匹配剔除的视觉任务。
原文依据 erroneous matches rarely cross source boundaries, enabling high-precision clustering
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论作为人类文化遗产的一项重要研究任务，艺术品与书法作品的复原具有重要意义。现有工作很少考虑面向多源碎片（即不保证碎片来自同一件艺术品）的复原任务。我们提出 ShreddingNet，一种用于多源手稿复原的由粗到细两阶段流程，且不受限制性条件的约束。所提出的粗阶段比较各碎片的特征，选出前 K 个候选并按来源对碎片进行聚类。该设计基于一个关键洞见：错误匹配很少跨越来源边界，因而能够实现高精度聚类。所提出的细粒度阶段对候选进行评估，给出匹配分数并从候选集中滤除错误匹配对，从而为全局拼装产生更精确的最终匹配对。在两个数据集上、超过 4000 张图像上进行的实验表明，平均重建 F1 分数达到 98.37%，比当前最优方法高出 5.72%，验证了该方法的有效性与鲁棒性。源代码见 github.com/tqychy/shreddingnet。
英文原文摘要 1221 字符
As an important research task of human cultural heritage, the restoration of artworks and calligraphy is of great significance. Seldom existing works have taken the multi-source (i.e., fragments are not ensured to be from the same piece of artworks) fragment-oriented restoration task into account. We propose ShreddingNet, a coarse-to-fine two-stage pipeline for multi-source manuscript restoration that operates without restrictive conditions. The proposed coarse stage compares the features of each fragment, selecting top-K candidates and clustering fragments by source. This design leverages the key insight that erroneous matches rarely cross source boundaries, enabling high-precision clustering. The proposed fine-grained stage evaluates candidates, yielding matching scores and filters out erroneous matching pairs from the candidate set; producing more precise final matching pairs for global assembly. Experiments conducted on more than 4,000 images from two datasets demonstrate the average reconstruction F1-score achieves 98.37%, which is 5.72% higher than the current state-of-the-art method, confirming the method's effectiveness and robustness. Source code is available at github.com/tqychy/shreddingnet.