Nautilus：用于可扩展网格生成的局部感知自编码器
Nautilus: Locality-aware Autoencoder for Scalable Mesh Generation
AI总结：
提出Nautilus局部感知自编码器，通过保留面邻近关系的分词算法和双流点条件器，实现高达5000面的高质量、可扩展网格生成。
摘要：
三角网格是3D应用的基础，能够实现高效修改和光栅化，同时保持与标准渲染管线的兼容性。然而，当前的自动网格生成方法通常依赖于缺乏网格固有的连续表面质量的中间表示。将这些表示转换为网格会产生密集且次优的输出。尽管最近的自回归方法在直接建模网格顶点和面方面展现出前景，但它们受到面数、可扩展性和结构保真度方面的限制。为了解决这些挑战，我们提出了Nautilus，一种用于艺术家级网格生成的局部感知自编码器，它利用流形网格的局部特性来实现结构保真度和高效表示。我们的方法引入了一种新颖的分词算法，该算法保留了面邻近关系并通过局部共享的顶点和边压缩序列长度，从而能够生成面数高达5,000的前所未有规模的网格。此外，我们开发了一种双流点条件器，提供多尺度几何指导，通过捕获细粒度几何特征来确保全局一致性和局部结构保真度。大量实验表明，Nautilus在保真度和可扩展性方面均显著优于现有最先进的方法。项目页面位于https://nautilusmeshgen.github.io。
英文摘要：
Triangle meshes are fundamental to 3D applications, enabling efficient modification and rasterization while maintaining compatibility with standard rendering pipelines. However, current automatic mesh generation methods typically rely on intermediate representations that lack the continuous surface quality inherent to meshes. Converting these representations into meshes produces dense, suboptimal outputs. Although recent autoregressive approaches demonstrate promise in directly modeling mesh vertices and faces, they are constrained by the limitation in face count, scalability, and structural fidelity. To address these challenges, we propose Nautilus, a locality-aware autoencoder for artist-like mesh generation that leverages the local properties of manifold meshes to achieve structural fidelity and efficient representation. Our approach introduces a novel tokenization algorithm that preserves face proximity relationships and compresses sequence length through locally shared vertices and edges, enabling the generation of meshes with an unprecedented scale of up to 5,000 faces. Furthermore, we develop a Dual-stream Point Conditioner that provides multi-scale geometric guidance, ensuring global consistency and local structural fidelity by capturing fine-grained geometric features. Extensive experiments demonstrate that Nautilus significantly outperforms state-of-the-art methods in both fidelity and scalability. The project page is at https://nautilusmeshgen.github.io.
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。
