提出新指标与坐标合并技术，提升网格生成的压缩效率。
FreeMesh: Boosting Mesh Generation with Coordinates Merging
- 引入无训练的PTME指标评估网格分词器性能。
- 通过坐标合并使现有分词器压缩比进一步提升。
- 适配多种分词方法，可直接集成于现有框架中。
自回归网格生成方法普遍采用预测下一个坐标的方式，但缺乏高效评估不同分词器性能的标准。本文提出一种无需训练的理论评估指标——每标记网格熵（Per-Token-Mesh-Entropy, PTME），用于分析现有网格分词器。基于PTME，我们设计了一种即插即用的分词优化技术——坐标合并，通过重组和合并坐标中高频模式，进一步提升分词器的压缩率。在MeshXL、MeshAnything V2和Edgerunner等多种分词方法上的实验验证了该方法的有效性。我们希望提出的PTME与坐标合并能增强现有分词器，并推动原生网格生成的发展。
原文摘要 · Abstract (English)
The next-coordinate prediction paradigm has emerged as the de facto standard in current auto-regressive mesh generation methods. Despite their effectiveness, there is no efficient measurement for the various tokenizers that serialize meshes into sequences. In this paper, we introduce a new metric Per-Token-Mesh-Entropy (PTME) to evaluate the existing mesh tokenizers theoretically without any training. Building upon PTME, we propose a plug-and-play tokenization technique called coordinate merging. It further improves the compression ratios of existing tokenizers by rearranging and merging the most frequent patterns of coordinates. Through experiments on various tokenization methods like MeshXL, MeshAnything V2, and Edgerunner, we further validate the performance of our method. We hope that the proposed PTME and coordinate merging can enhance the existing mesh tokenizers and guide the further development of native mesh generation.
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