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.
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.
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:
# 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.
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.
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:
@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}
}