---
license: mit
task_categories:
- image-segmentation
- visual-question-answering
language:
- en
tags:
- panoramic-vision
- active-perception
- referring-segmentation
- 360-degree
- spatial-reasoning
size_categories:
- 1K<n<10K
pretty_name: Active Panoramic Referring Segmentation
---

# APRS Dataset

[![Paper](https://img.shields.io/badge/arXiv-2607.02497-b31b1b.svg)](https://arxiv.org/abs/2607.02497v1)
[![Project Page](https://img.shields.io/badge/Project-Page-green)](https://henghuiding.com/APRS/)
[![GitHub](https://img.shields.io/badge/GitHub-Code-black)](https://github.com/FudanCVL/APRS)

## Dataset Description

**APRS (Active Panoramic Referring Segmentation)** is a large-scale benchmark dataset for active perception in 360° panoramic environments. Unlike passive referring segmentation that processes static images, APRS requires agents to **actively explore** continuous panoramic scenes by adjusting viewing directions to seek and segment targets based on natural language instructions.

### Dataset Summary

- 🎯 **7,420 samples** across **4,971 diverse panoramic scenes**
- 🏠 Indoor and outdoor 360° environments
- 📐 **Four types of spatial referring expressions**:
  - **Egocentric**: First-person directional references (e.g., "look left to find...")
  - **Unique-Attribute**: Distinctive object features (e.g., "the red sofa")
  - **Allocentric**: Third-person spatial relations (e.g., "the chair near the window")
  - **Multi-hop**: Complex relational reasoning (e.g., "look to the left to find the bed, then find the lamp on the table next to it")


## Data Fields

Each sample contains:

- `Filename`: Image filename (panoramic view)
- `Img_W`, `Img_H`, `Aspect_Ratio`: Image dimensions and aspect ratio
- `Pt_X`, `Pt_Y`: Target point pixel coordinates
- `Pt_X_Norm`, `Pt_Y_Norm`: Normalized point coordinates [0, 1]
- `Box_X`, `Box_Y`, `Box_W`, `Box_H`: Bounding box (x, y, width, height) in pixels
- `Box_X_Norm`, `Box_Y_Norm`, `Box_W_Norm`, `Box_H_Norm`: Normalized bounding box [0, 1]
- `Pt_Theta`, `Pt_Phi`: Target point spherical coordinates (degrees)
  - Theta (θ): Horizontal angle [-180°, 180°]
  - Phi (φ): Vertical angle [-90°, 90°]
- `Box_Theta`, `Box_Phi`: Bounding box center spherical coordinates (degrees)
- `Description`: Natural language referring expression
- `Category`: Spatial reference type
  - `EGO`: Egocentric (first-person directional)
  - `ALLO`: Allocentric (third-person spatial relations)
  - `UNIQ`: Unique attributes
  - `MULT`: Multi-hop reasoning

## Usage

### Load with APRS Dataset Class 

```python
from aprs import APRSDataset

# Load directly from HuggingFace Hub
dataset = APRSDataset.from_hub(repo_id="FudanCVL/APRS_dataset", split="train")

# Access samples
sample = dataset[0]
print(f"Instruction: {sample.instruction}")
print(f"Category: {sample.category}")
print(f"Initial view: θ={sample.init_theta:.1f}°, φ={sample.init_phi:.1f}°")
print(f"Target view: θ={sample.target_theta:.1f}°, φ={sample.target_phi:.1f}°")

# Load panoramic image (BGR numpy array)
image = sample.load_image()

# Get bounding box
box_pixels = sample.box_pixels()  # (x, y, w, h) in pixels
```


### Interactive 360° Visualization

```bash
# Clone the official repository
git clone https://github.com/FudanCVL/APRS.git
cd APRS

# Install dependencies
pip install -e ".[viewer]"

# Launch interactive viewer directly from HuggingFace
python tools/viewer_360.py --hf --split test --index 0

# Or download dataset and view locally
python tools/viewer_360.py --root APRS_dataset --split test --index 0
```

**Viewer Controls**: 
- 🖱️ Drag mouse to rotate view
- ⌨️ WASD or Arrow keys for navigation
- R to reset to initial view
- 🟩 Green box shows target region

## Citation

If you use this dataset, please cite:

```bibtex
@article{tang2026seek,
  title={Seek to Segment: Active Perception for Panoramic Referring Segmentation},
  author={Tang, Song and Hu, Shuming and Shuai, Xincheng and Ding, Henghui and Jiang, Yu-Gang},
  journal={arXiv preprint arXiv:2607.02497},
  year={2026}
}
```

## Contact

- **Song Tang**: tangsong322@gmail.com
- **Henghui Ding**: henghuiding@gmail.com

## License

This dataset is released under the [MIT License](https://opensource.org/licenses/MIT).

## Links

- 📄 **Paper**: [arXiv:2607.02497](https://arxiv.org/abs/2607.02497v1)
- 🌐 **Project Page**: [https://henghuiding.com/APRS/](https://henghuiding.com/APRS/)
- 💻 **Code**: [https://github.com/FudanCVL/APRS](https://github.com/FudanCVL/APRS)