保持拓扑结构的自回归网格生成：如编织丝绸般的方法
Topology-Preserved Auto-regressive Mesh Generation in the Manner of Weaving Silk
AI总结：
本文提出了一种保持拓扑结构的自回归网格生成方法，通过顶点分层和排序实现规范拓扑框架，提升网格生成的几何完整性和压缩效率。
摘要：
现有的自回归网格生成方法在拓扑保持方面效果不佳，这对于实际应用至关重要。这一限制源于先前的网格标记方法将网格视为等同三角形的简单集合，缺乏在生成过程中对整体拓扑结构的意识。为了解决这个问题，我们提出了一种新的网格标记算法，通过顶点分层和排序提供一个规范的拓扑框架，确保生成的网格中包含关键的几何属性，包括流形性、封闭性、面法线一致性以及部分意识。通过压缩比和每面位数进行测量，我们还实现了最先进的压缩效率。此外，我们引入了一个在线非流形数据处理算法和一个训练重采样策略，以扩大可训练数据集的规模并避免昂贵的手动数据整理。实验结果展示了我们方法的有效性，不仅展示了复杂的网格生成，还显著提高了几何完整性。
英文摘要：
Existing auto-regressive mesh generation approaches suffer from ineffective topology preservation, which is crucial for practical applications. This limitation stems from previous mesh tokenization methods treating meshes as simple collections of equivalent triangles, lacking awareness of the overall topological structure during generation. To address this issue, we propose a novel mesh tokenization algorithm that provides a canonical topological framework through vertex layering and ordering, ensuring critical geometric properties including manifoldness, watertightness, face normal consistency, and part awareness in the generated meshes. Measured by Compression Ratio and Bits-per-face, we also achieved state-of-the-art compression efficiency. Furthermore, we introduce an online non-manifold data processing algorithm and a training resampling strategy to expand the scale of trainable dataset and avoid costly manual data curation. Experimental results demonstrate the effectiveness of our approach, showcasing not only intricate mesh generation but also significantly improved geometric integrity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
