Improve dataset card: Add task categories, abstract, correct links, and sample usage

README.md

@@ -1,25 +1,51 @@
 ---
 license: cc-by-4.0
+task_categories:
+- GRAPH_MACHINE_LEARNING
+tags:
+- 3d
+- part-segmentation
+- question-answering
+- object-understanding
 ---
 
 # [Neurips 2025 DB] PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding
 Official dataset release for _PartNeXt: A Next-Generation Dataset for Fine-Grained and Hierarchical 3D Part Understanding_.
 
-[![arXiv](https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white)](https://authoritywang.github.io/partnext/)
+[![arXiv](https://img.shields.io/badge/arXiv-Paper-red?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2510.20155)
 [![Project Page](https://img.shields.io/badge/Project_Page-Website-green?logo=homepage&logoColor=white)](https://authoritywang.github.io/partnext/)
 [![🤗 Hugging Face Dataset](https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Dataset-blue)](https://huggingface.co/datasets/AuWang/PartNeXt)
 
+## Abstract
+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.
+
 **[Penghao Wang](https://authoritywang.github.io/), Yiyang He, Xin Lv, Yukai Zhou, [Lan Xu](https://www.xu-lan.com/), [Jingyi Yu](http://www.yu-jingyi.com/cv/), [Jiayuan Gu†](https://jiayuan-gu.github.io/)**
 
 **ShanghaiTech University**
 
 **Neurips 2025 Dataset and Benchmark Track**
 
-| [Project Page](https://authoritywang.github.io/partnext/) | [Paper]() | [Dataset](https://huggingface.co/datasets/AuWang/PartNeXt) | [Dataset Toolkit](https://github.com/AuthorityWang/PartNeXt) | [Benchmark code (Soon)]() | [Annotation code (Soon)]() |<br>
+| [Project Page](https://authoritywang.github.io/partnext/) | [Paper](https://huggingface.co/papers/2510.20155) | [Code](https://github.com/AuthorityWang/PartNeXt) | [Dataset](https://huggingface.co/datasets/AuWang/PartNeXt) | [Dataset Toolkit](https://github.com/AuthorityWang/PartNeXt_lib) | [Benchmark code (Soon)]() | [Annotation code (Soon)]() |<br>
+
+## Sample Usage
 
-## Usage
+You can download the dataset using the Hugging Face CLI:
+```bash
+hf download --repo-type dataset AuWang/PartNeXt_mesh --local-dir /your/own/path
+hf download --repo-type dataset AuWang/PartNeXt --local-dir /your/own/path
+```
 
-For usage, please refer to our dataset toolkit repo [https://github.com/AuthorityWang/PartNeXt](https://github.com/AuthorityWang/PartNeXt)
+The PartNeXt dataset toolkit can be installed via pip:
+```bash
+pip install partnext
+```
+Alternatively, you can install it from source:
+```bash
+git clone https://github.com/AuthorityWang/PartNeXt_lib.git
+cd PartNeXt_lib
+pip install -e .
+```
+For examples on how to use the toolkit, please refer to `example/toolkit_example.py` in the [Dataset Toolkit repository](https://github.com/AuthorityWang/PartNeXt_lib).
 
 ## Acknowledgement
 Our PartNeXt dataset is based on [Objaverse](https://objaverse.allenai.org/), [ABO](https://amazon-berkeley-objects.s3.amazonaws.com/index.html), [3D-Future](https://tianchi.aliyun.com/dataset/98063), thanks for these awesome datasets. If there is any license issue, please contact us and we will remove the data.