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.

翻译：现有的自回归网格生成方法在保持拓扑结构方面存在不足，而拓扑保持对于实际应用至关重要。这一局限性源于先前的网格标记化方法将网格视为简单等效三角形的集合，在生成过程中缺乏对整体拓扑结构的感知。为解决这一问题，我们提出了一种新颖的网格标记化算法，通过顶点分层与排序提供规范化的拓扑框架，确保生成网格具有流形性、水密性、面法向一致性及部件感知等关键几何特性。在压缩率与每面比特数指标上，我们的方法同时达到了最优的压缩效率。此外，我们引入了在线非流形数据处理算法和训练重采样策略，以扩展可训练数据集的规模，避免昂贵的人工数据标注工作。实验结果验证了所提方法的有效性，不仅实现了复杂网格的生成，还显著提升了几何完整性。