Add metadata and improve dataset card
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by nielsr HF Staff - opened
README.md
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</div>
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### 1. Unzip the Data
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Before using the data, please download and extract the zip files. You can use the following commands in your terminal:
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│ └── ...
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├── scene0000_01/
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└── ...
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```
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---
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task_categories:
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- zero-shot-object-detection
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---
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# Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding
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This repository contains the ScanNet dataset (3D scene data and 2D frame data) and refined annotations used for the paper [Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding](https://huggingface.co/papers/2604.00528).
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TAB is a dynamic agentic framework designed for zero-shot 3D Visual Grounding (3D-VG). By operating directly on raw RGB-D streams, TAB reformulates 3D grounding from a static proposal matching task into an active semantic reasoning and geometric reconstruction process.
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- **GitHub:** [https://github.com/WHB139426/TAB-Agent](https://github.com/WHB139426/TAB-Agent)
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- **Paper:** [https://huggingface.co/papers/2604.00528](https://huggingface.co/papers/2604.00528)
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## ScanNet Data Preparation
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This repository contains the ScanNet dataset split into two main compressed archives: 3D scene data and 2D frame data.
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### 1. Unzip the Data
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Before using the data, please download and extract the zip files. You can use the following commands in your terminal:
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│ └── ...
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├── scene0000_01/
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└── ...
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```
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## Citation
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If you find this work useful, please consider citing:
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```bibtex
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@misc{wang2026thinkactbuildagentic,
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title={Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding},
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author={Haibo Wang and Zihao Lin and Zhiyang Xu and Lifu Huang},
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year={2026},
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eprint={2604.00528},
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archivePrefix={arXiv},
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primaryClass={cs.CV},
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url={https://arxiv.org/abs/2604.00528},
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}
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```
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