部件识别与命名：三维零件分割及标注

摘要

我们致力于解决语义三维部件分割问题：将物体分解为具有意义名称的部件。尽管现有数据集包含部件标注，但其定义标准不一，限制了稳健模型的训练。现有方法或生成未标注的分解结果，或在缺乏完整形状标注的情况下检索单一部件。我们提出ALIGN-Parts方法，将部件命名构建为直接集合对齐任务。该方法将形状分解为部件单元——一种隐式三维部件表征，并通过二分图匹配与部件描述建立对应关系。我们融合了三维部件场的几何线索、多视角视觉特征的外观信息，以及语言模型生成的功能描述所蕴含的语义知识。文本对齐损失确保部件单元与文本共享嵌入空间，在数据充足条件下可实现理论上的开放词汇匹配。我们这种高效新颖的单次三维部件分割与命名方法，可应用于多项下游任务，包括作为可扩展的标注引擎。由于模型支持对任意描述的零样本匹配，并能对已知类别进行置信度校准预测，通过人工验证，我们构建了整合PartNet、3DCoMPaT++和Find3D的统一本体，包含1,794个独特三维部件。同时展示了新构建的Tex-Parts数据集样例，并针对命名三维部件分割任务提出了两项创新性评估指标。

English

We address semantic 3D part segmentation: decomposing objects into parts with meaningful names. While datasets exist with part annotations, their definitions are inconsistent across datasets, limiting robust training. Previous methods produce unlabeled decompositions or retrieve single parts without complete shape annotations. We propose ALIGN-Parts, which formulates part naming as a direct set alignment task. Our method decomposes shapes into partlets - implicit 3D part representations - matched to part descriptions via bipartite assignment. We combine geometric cues from 3D part fields, appearance from multi-view vision features, and semantic knowledge from language-model-generated affordance descriptions. Text-alignment loss ensures partlets share embedding space with text, enabling a theoretically open-vocabulary matching setup, given sufficient data. Our efficient and novel, one-shot, 3D part segmentation and naming method finds applications in several downstream tasks, including serving as a scalable annotation engine. As our model supports zero-shot matching to arbitrary descriptions and confidence-calibrated predictions for known categories, with human verification, we create a unified ontology that aligns PartNet, 3DCoMPaT++, and Find3D, consisting of 1,794 unique 3D parts. We also show examples from our newly created Tex-Parts dataset. We also introduce 2 novel metrics appropriate for the named 3D part segmentation task.

部件识别与命名：三维零件分割及标注

Name That Part: 3D Part Segmentation and Naming

摘要

Support