---
language:
- en
license:
- mit
---

# ADT Dataset

## Dataset Description
This dataset contains Aria Digital Twin (ADT) sequences with various sensor data and annotations, including 2D/3D bounding boxes, trajectories, eye gaze data, and VRS recordings.

## Quick Start
```python
from adt_dataset_loader import ADTDatasetLoader

# Load entire dataset
loader = ADTDatasetLoader("ariakang/ADT-test")

# Load specific sequence
loader = ADTDatasetLoader("ariakang/ADT-test", sequence_name="Apartment_release_clean_seq131_M1292")
```

## Installation
```bash
# Install required packages
pip install datasets pandas
```

## Dataset Structure
Each sequence contains:
- VRS Files:
  - video.vrs
  - synthetic_video.vrs
  - segmentations.vrs
  - depth_images.vrs
- CSV Data:
  - 2D/3D bounding boxes
  - Aria device trajectories
  - Eye gaze data
  - Scene objects
- JSON Data:
  - Instance annotations
  - Metadata
- MPS Data:
  - Eye gaze processing
  - SLAM results

## Flexible Loading Options

### 1. Load Entire Dataset
```python
# Initialize loader with all sequences
loader = ADTDatasetLoader("ariakang/ADT-test")

# See available sequences and data types
available_files = loader.get_available_files()
print("Available files:", available_files)

# Load all data types
bbox_2d = loader.load_2d_bounding_boxes()
bbox_3d = loader.load_3d_bounding_boxes()
trajectory = loader.load_aria_trajectory()
eyegaze = loader.load_eyegaze()
metadata = loader.load_metadata()
slam_data = loader.load_mps_slam()
```

### 2. Load Specific Sequences
```python
# Load a specific sequence
loader = ADTDatasetLoader(
    "ariakang/ADT-test",
    sequence_name="Apartment_release_clean_seq131_M1292"
)

# Load data from this sequence
bbox_2d = loader.load_2d_bounding_boxes()
trajectory = loader.load_aria_trajectory()
```

### 3. Load Selected Data Types
```python
# Initialize loader for specific sequence
loader = ADTDatasetLoader("ariakang/ADT-test", "Apartment_release_clean_seq131_M1292")

# Load only 2D bounding boxes and VRS info
bbox_2d = loader.load_2d_bounding_boxes()
vrs_info = loader.get_vrs_files_info()

# Get paths to specific VRS files
video_vrs = [f for f in vrs_info if f['filename'] == 'video.vrs'][0]
print(f"Video VRS path: {video_vrs['path']}")

# Load only SLAM data
slam_data = loader.load_mps_slam()
closed_loop = slam_data['closed_loop']  # Get specific SLAM component
```

## Available Data Types and Methods

### Main Data Types
```python
# Bounding Boxes and Trajectories
bbox_2d = loader.load_2d_bounding_boxes()
bbox_3d = loader.load_3d_bounding_boxes()
trajectory = loader.load_aria_trajectory()

# Eye Gaze and Scene Data
eyegaze = loader.load_eyegaze()
scene_objects = loader.load_scene_objects()

# Metadata and Instances
metadata = loader.load_metadata()
instances = loader.load_instances()

# MPS Data
eye_gaze_data = loader.load_mps_eye_gaze()  # Returns dict with 'general' and 'summary'
slam_data = loader.load_mps_slam()  # Returns dict with various SLAM components
```

### VRS Files
```python
# Get VRS file information
vrs_info = loader.get_vrs_files_info()

# Example: Access specific VRS file info
for vrs_file in vrs_info:
    print(f"File: {vrs_file['filename']}")
    print(f"Path: {vrs_file['path']}")
    print(f"Size: {vrs_file['size_bytes'] / 1024 / 1024:.2f} MB")
```

### Custom Loading
```python
# Load any file by name
data = loader.load_file_by_name("your_file_name.csv")
```

## Data Format Examples

### 2D Bounding Boxes
```python
bbox_2d = loader.load_2d_bounding_boxes()
print(bbox_2d.columns)
# Columns: ['object_uid', 'timestamp[ns]', 'x_min[pixel]', 'x_max[pixel]', 'y_min[pixel]', 'y_max[pixel]']
```

### Aria Trajectory
```python
trajectory = loader.load_aria_trajectory()
print(trajectory.columns)
# Columns: ['timestamp[ns]', 'x', 'y', 'z', 'qx', 'qy', 'qz', 'qw']
```

### MPS SLAM Data
```python
slam_data = loader.load_mps_slam()
# Components:
# - closed_loop: DataFrame with closed-loop trajectory
# - open_loop: DataFrame with open-loop trajectory
# - calibration: Calibration parameters
```

## Error Handling
```python
try:
    data = loader.load_file_by_name("non_existent_file.csv")
except ValueError as e:
    print(f"Error: {e}")
```

## Notes
- All CSV files are loaded as pandas DataFrames
- JSON/JSONL files are loaded as Python dictionaries/lists
- VRS files are not loaded into memory, only their metadata and paths are provided
- Use `get_available_files()` to see all available data in your sequence

## Repository Structure
VRS files are stored in sequence-specific folders:
`sequences/{sequence_name}/vrs_files/`