LoST：面向三维形状的语义层级分词
LoST: Level of Semantics Tokenization for 3D Shapes
把 3D 形状 token 的排序从几何 LoD 换成语义显著性排序，并用 DINO 特征空间的关系结构对齐 3D 潜空间，可直接复用到任何基于自回归或潜变量的 3D 生成与形状表征学习流程中。
原文依据 we introduce Relational Inter-Distance Alignment (RIDA), a novel 3D semantic alignment loss, inspired by relational knowledge distillation
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论分词是各类模态生成建模中的基础技术。尤其对于近年来成为三维生成重要选择的自回归模型而言，分词起着关键作用。然而，三维形状的最优分词仍是一个开放问题。当前最优方法主要依赖几何细节层级（LoD）层次结构，这类结构原本是为渲染与压缩而设计的。这些空间层次往往分词效率低下，且缺乏面向自回归建模的语义连贯性。我们提出语义层级分词（LoST），按语义显著性对 token 排序，使得较早的前缀即可解码出具备主要语义的完整、合理形状，而后续 token 则细化实例特有的几何与语义细节。为训练 LoST，我们引入关系互距离对齐（RIDA），这是一种受关系知识蒸馏启发的新型三维语义对齐损失，用于将三维形状潜空间的关系结构与语义 DINO 特征空间的关系结构对齐。实验表明，LoST 取得了最优的重建性能，在几何与语义重建指标上均大幅超越此前基于 LoD 的三维形状分词器。此外，LoST 实现了高效、高质量的自回归三维生成，并支持语义检索等下游任务，而所用 token 仅为先前三维自回归模型的 0.1%–10%。代码见项目网站 https://lost3d.github.io/。
英文原文摘要 1521 字符
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, inspired by relational knowledge distillation, 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 3D AR models. Code is available via the project website at https://lost3d.github.io/