| --- |
| task_categories: |
| - zero-shot-object-detection |
| --- |
| |
| # Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding |
|
|
| 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). |
|
|
| 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. |
|
|
| - **GitHub:** [https://github.com/WHB139426/TAB-Agent](https://github.com/WHB139426/TAB-Agent) |
| - **Paper:** [https://huggingface.co/papers/2604.00528](https://huggingface.co/papers/2604.00528) |
|
|
| ## ScanNet Data Preparation |
|
|
| This repository contains the ScanNet dataset split into two main compressed archives: 3D scene data and 2D frame data. |
|
|
| ### 1. Unzip the Data |
| Before using the data, please download and extract the zip files. You can use the following commands in your terminal: |
|
|
| ```bash |
| # Unzip the 3D meshes and annotations (10.4 GB) |
| unzip scannet-dataset.zip |
| |
| # Unzip the 2D RGB-D frames and camera poses (99.1 GB) |
| unzip scannet-frames.zip |
| ``` |
|
|
| ### 2. Directory Structure |
|
|
| After unzipping, you will have two main directories organized by `scene_id`. |
|
|
| #### `scannet-dataset/` (3D Meshes & Annotations) |
| This folder contains the core 3D spatial data, including the reconstructed meshes, semantic labels, and instance aggregations. |
|
|
| ```text |
| scannet-dataset/ |
| ├── scene0000_00/ |
| │ ├── scene0000_00_vh_clean_2.ply # Cleaned 3D mesh |
| │ ├── scene0000_00_vh_clean_2.labels.ply # 3D mesh with semantic labels |
| │ ├── scene0000_00_vh_clean_2.0.010000.segs.json # Over-segmentation file |
| │ ├── scene0000_00.aggregation.json # Instance aggregation data |
| │ └── scene0000_00.txt # Scene metadata and properties |
| ├── scene0000_01/ |
| └── ... |
| ``` |
|
|
| #### `scannet-frames/` (2D Frames & Poses) |
| This folder contains the extracted 2D multi-view data for each scene, which is essential for tasks requiring RGB, depth, or camera pose alignments. |
|
|
| ```text |
| scannet-frames/ |
| ├── scene0000_00/ |
| │ ├── 00000.jpg # RGB color frame |
| │ ├── 00000.png # Depth map / Mask |
| │ ├── 00000.txt # Camera pose matrix for this specific frame |
| │ ├── 00001.jpg |
| │ ├── 00001.png |
| │ ├── 00001.txt |
| │ └── ... |
| ├── scene0000_01/ |
| └── ... |
| ``` |
|
|
| ## Citation |
|
|
| If you find this work useful, please consider citing: |
|
|
| ```bibtex |
| @article{wang2026think, |
| title={Think, Act, Build: An Agentic Framework with Vision Language Models for Zero-Shot 3D Visual Grounding}, |
| author={Wang, Haibo and Lin, Zihao and Xu, Zhiyang and Huang, Lifu}, |
| journal={arXiv preprint arXiv:2604.00528}, |
| year={2026} |
| } |
| ``` |
