通过由粗到细语言引导的通用三维形状匹配
Universal 3D Shape Matching via Coarse-to-Fine Language Guidance
把类无关三维分割得到的部件与 MLLM/VLM 的文本嵌入对齐，用语言作为跨类别形状的语义桥梁，再以基于排名的对比学习把粗粒度部件匹配细化为稠密对应，可直接迁移到跨类别非等距形状配准、纹理/属性迁移等任务。
原文依据 lift "coarse" semantic cues into "fine" correspondence
摘要译文
由模型翻译 · 术语统一 · 请对照原文核对关键结论建立形状之间的稠密对应关系是计算机视觉与图形学中的一项关键任务，而以往方法依赖于近似等距假设与同质主体类型（即仅适用于人体形状）。然而，为跨类别物体构建语义对应仍然具有挑战性，且受到的关注相对较少。为此，我们提出 UniMatch，一个语义感知的由粗到细框架，用于在不受物体类别限制的强非等距形状之间构建稠密语义对应。其核心思想是将“粗”粒度语义线索提升为“细”粒度对应，这通过两个阶段实现。在“粗”阶段，我们执行类无关的三维分割以获得互不重叠的语义部件，并提示多模态大语言模型（MLLM）识别部件名称。随后，我们采用预训练的视觉语言模型（VLM）提取文本嵌入，从而构建相匹配的语义部件。在“细”阶段，我们利用这些粗粒度对应，通过一种专门的基于排名的对比方案来引导稠密对应的学习。得益于类无关分割、语言引导以及基于排名的对比学习，我们的方法可通用于任意物体类别，且无需预定义的部件候选，从而实现了类间与非等距形状的通用匹配。大量实验表明，UniMatch 在各种具有挑战性的场景中始终优于对比方法。
英文原文摘要 1550 字符
Establishing dense correspondences between shapes is a crucial task in computer vision and graphics, while prior approaches depend on near-isometric assumptions and homogeneous subject types (i.e., only operate for human shapes). However, building semantic correspondences for cross-category objects remains challenging and has received relatively little attention. To achieve this, we propose UniMatch, a semantic-aware, coarse-to-fine framework for constructing dense semantic correspondences between strongly non-isometric shapes without restricting object categories. The key insight is to lift "coarse" semantic cues into "fine" correspondence, which is achieved through two stages. In the "coarse" stage, we perform class-agnostic 3D segmentation to obtain non-overlapping semantic parts and prompt multimodal large language models (MLLMs) to identify part names. Then, we employ pretrained vision language models (VLMs) to extract text embeddings, enabling the construction of matched semantic parts. In the "fine" stage, we leverage these coarse correspondences to guide the learning of dense correspondences through a dedicated rank-based contrastive scheme. Thanks to class-agnostic segmentation, language guiding, and rank-based contrastive learning, our method is versatile for universal object categories and requires no predefined part proposals, enabling universal matching for inter-class and non-isometric shapes. Extensive experiments demonstrate UniMatch consistently outperforms competing methods in various challenging scenarios.