PartNeXt：面向细粒度与层级化三维部件理解的下一代数据集

摘要

在构件层面理解物体是推动计算机视觉、图形学和机器人技术发展的基础。尽管PartNet等数据集推动了三维部件理解的发展，但其依赖无纹理几何体和专家标注的特性限制了可扩展性和可用性。我们推出新一代数据集PartNeXt，通过5大类别下超过23,000个高质量带纹理三维模型及其细粒度层次化部件标注，有效解决了这些局限性。我们在两项任务上对PartNeXt进行基准测试：（1）类别无关部件分割——现有前沿方法（如PartField、SAMPart3D）在处理细粒度和末端级部件时表现不佳；（2）三维部件中心问答——这个针对3D-LLMs的新基准揭示了开放词汇部件定位领域的显著不足。此外，基于PartNeXt训练的Point-SAM模型相比PartNet实现显著性能提升，印证了该数据集在质量与多样性方面的优越性。通过融合可扩展标注、纹理感知标签和多任务评估，PartNeXt为结构化三维理解研究开辟了新途径。

English

Understanding objects at the level of their constituent parts is fundamental to advancing computer vision, graphics, and robotics. While datasets like PartNet have driven progress in 3D part understanding, their reliance on untextured geometries and expert-dependent annotation limits scalability and usability. We introduce PartNeXt, a next-generation dataset addressing these gaps with over 23,000 high-quality, textured 3D models annotated with fine-grained, hierarchical part labels across 50 categories. We benchmark PartNeXt on two tasks: (1) class-agnostic part segmentation, where state-of-the-art methods (e.g., PartField, SAMPart3D) struggle with fine-grained and leaf-level parts, and (2) 3D part-centric question answering, a new benchmark for 3D-LLMs that reveals significant gaps in open-vocabulary part grounding. Additionally, training Point-SAM on PartNeXt yields substantial gains over PartNet, underscoring the dataset's superior quality and diversity. By combining scalable annotation, texture-aware labels, and multi-task evaluation, PartNeXt opens new avenues for research in structured 3D understanding.

PartNeXt：面向细粒度与层级化三维部件理解的下一代数据集

PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding

摘要

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