PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D
  Part Understanding
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.
6901b7bd646208eac0d1f567
Penghao Wang
claimed_verified
2025-10-29T12:40:52.365Z
6462120032083c28b408e754
/avatars/f02c6d23f03420b716d995c92f8e2213.svg
Penghao Wang
AuWang
user
AuWang
6901b7bd646208eac0d1f568
Yiyang He
6901b7bd646208eac0d1f569
Xin Lv
6901b7bd646208eac0d1f56a
Yukai Zhou
6901b7bd646208eac0d1f56b
Lan Xu
6901b7bd646208eac0d1f56c
Jingyi Yu
6901b7bd646208eac0d1f56d
Jiayuan Gu
PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D
  Part Understanding
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, a high-quality, textured 3D dataset with fine-grained part labels, improves performance in class-agnostic part segmentation and 3D part-centric question answering, highlighting gaps in open-vocabulary part grounding.
PartNet PartNeXt 3D part understanding fine-grained part labels class-agnostic part segmentation 3D part-centric question answering open-vocabulary part grounding Point-SAM structured 3D understanding