PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D
  Part Understanding
Authors: name: Penghao Wang, name: Yiyang He, name: Xin Lv, name: Yukai Zhou, name: Lan Xu, name: Jingyi Yu, name: Jiayuan Gu

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.