Tokenization is a fundamental technique in the generative modeling of various modalities. In particular, it plays a critical role in autoregressive (AR) models, which have recently emerged as a compelling option for 3D generation. However, optimal tokenization of 3D shapes remains an open question. State-of-the-art (SOTA) methods primarily rely on geometric level-of-detail (LoD) hierarchies, originally designed for rendering and compression. These spatial hierarchies are often token-inefficient and lack semantic coherence for AR modeling. We propose Level-of-Semantics Tokenization (LoST), which orders tokens by semantic salience, such that early prefixes decode into complete, plausible shapes that possess principal semantics, while subsequent tokens refine instance-specific geometric and semantic details. To train LoST, we introduce Relational Inter-Distance Alignment (RIDA), a novel 3D semantic alignment loss that aligns the relational structure of the 3D shape latent space with that of the semantic DINO feature space. Experiments show that LoST achieves SOTA reconstruction, surpassing previous LoD-based 3D shape tokenizers by large margins on both geometric and semantic reconstruction metrics. Moreover, LoST achieves efficient, high-quality AR 3D generation and enables downstream tasks like semantic retrieval, while using only 0.1%-10% of the tokens needed by prior AR models.

翻译：分词是多模态生成建模中的基础技术，尤其在近期成为三维生成领域热门方向的自回归模型中扮演关键角色。然而，三维形状的最优分词仍是一个开放问题。当前最先进方法主要依赖最初为渲染与压缩设计的几何细节层级(LoD)层次结构，这些空间层次结构通常存在分词效率低、缺乏语义连贯性等缺陷，不利于自回归建模。我们提出语义层级分词(LoST)，按语义显著性对词元排序，使早期前缀解码为具备主要语义的完整合理形状，后续词元则细化实例特定的几何与语义细节。为训练LoST，我们引入关系性距离对齐(RIDA)——一种新型三维语义对齐损失函数，用于对齐三维形状隐空间的关系结构与语义DINO特征空间的关系结构。实验表明，LoST在几何与语义重建指标上均大幅超越现有基于LoD的三维形状分词器，达到最先进重建水平。此外，LoST能实现高效高质量的自回归三维生成，仅需先前自回归模型0.1%-10%的词元数量即可支持语义检索等下游任务。