README.md

---
license: cc-by-4.0
task_categories:
- image-classification
- robotics
- computer-vision
tags:
- gravity-estimation
- visual-inertial
- IMU
- benchmark
- mobile-robotics
size_categories:
- 100K<n<1M
---

# GravCal: Large-Scale Orientation-Diverse Dataset for IMU Gravity Calibration

**NeurIPS 2026 Evaluations & Datasets Track**

## Dataset Description

GravCal is a large-scale dataset specifically designed for single-image IMU gravity calibration. The dataset addresses a critical gap in existing visual-inertial datasets, which exhibit severe upright-pose bias with most frames captured near canonical orientations.

### Key Features

- **148,000+ frames** with diverse camera orientations
- **Explicit coverage** of extreme tilts and rotations (0-180°)
- **Paired data**: RGB image + noisy IMU prior + VIO ground truth
- **Real-world IMU noise** from Mahony filter integration
- **Diverse scenes**: Indoor/outdoor with varying lighting conditions
- **High-quality labels**: VIO-derived gravity with sub-degree accuracy

### Dataset Statistics

| Property | Value |
|----------|-------|
| Total Frames | 148,000+ |
| Image Resolution | 640×480 |
| Rotation Coverage | 0-180° (uniform) |
| Scene Types | Indoor, Outdoor, Mixed lighting |
| Train/Val/Test Split | 70% / 10% / 20% |

## Dataset Structure

### Data Instances

Each instance contains:

```python
{
    "image_id": "sequence_001_frame_0042",
    "image": PIL.Image,
    "gravity_gt": [0.0234, -0.1234, -0.9922],  # VIO ground truth (3D unit vector)
    "gravity_prior": [0.0456, -0.1456, -0.9856],  # Mahony filter prior (3D unit vector)
    "scene_type": "indoor",  # indoor | outdoor
    "split": "train",  # train | val | test
    "prior_error": 12.3  # Angular error in degrees
}
```

### Data Fields

- `image_id`: Unique identifier for the frame
- `image`: RGB image (640×480 JPEG)
- `gravity_gt`: Ground-truth gravity direction (3D unit vector) from VIO
- `gravity_prior`: Noisy gravity prior (3D unit vector) from Mahony filter
- `scene_type`: Scene category (indoor/outdoor)
- `split`: Data split (train/val/test)
- `prior_error`: Angular error between prior and ground truth (degrees)

### Data Splits

| Split | Frames | Percentage |
|-------|--------|-----------|
| Train | ~103,600 | 70% |
| Val | ~14,800 | 10% |
| Test | ~29,600 | 20% |

Splits are created by **sequence**, not random sampling, to prevent data leakage.

## Dataset Creation

### Source Data

- **Hardware**: iPhone 12 Pro / 13 Pro Max
- **Sensors**: Wide camera (12MP) + 6-axis IMU
- **Collection**: Diverse indoor/outdoor environments with explicit rotation diversity
- **Duration**: ~150 hours of recording across 300+ sequences

### Data Collection

Data was collected with explicit instructions to achieve orientation diversity:
- Systematic rotation sampling
- Coverage of extreme tilts (>60°)
- Various motion patterns (static, walking, running, rotation)
- Multiple lighting conditions (daylight, indoor, low-light)

### Annotations

**Ground Truth**: 
- Extracted from ARKit Visual-Inertial Odometry (VIO)
- Validated against public benchmarks (EuRoC, TUM-VI)
- Quality filtering based on tracking confidence

**IMU Prior**:
- Generated using Mahony filter (Kp=0.5, Ki=0.0)
- Reflects real-world inertial drift
- Error range: 0-90° (concentrated around 10-30°)

### Privacy and Ethical Considerations

- Frames containing identifiable individuals are excluded or face-blurred
- Data collected in public/semi-public spaces with appropriate permissions
- No personally identifiable information (PII) included
- Dataset intended for research use only

## Benchmark Evaluation

### Evaluation Protocols

We provide multiple evaluation settings:

1. **In-Domain General**: Standard test set
2. **Rotation-Stratified**: By prior error (0-10°, 10-30°, 30-60°, >60°)
3. **Scene-Specific**: Indoor / Outdoor / Low-light
4. **Cross-Dataset**: EuRoC, TUM-VI, UZH-FPV

### Baseline Results

| Method | Mean Error | Median Error | <10° (%) |
|--------|-----------|--------------|----------|
| IMU Prior (raw) | 22.02° | 18.45° | 42.3% |
| Image-only | 18.76° | 15.23° | 48.1% |
| **Baseline (ours)** | **14.24°** | **11.32°** | **61.7%** |

## Usage

### Loading the Dataset

```python
from datasets import load_dataset

# Load full dataset
dataset = load_dataset("gravcal-neurips2026/gravcal")

# Load only review sample (faster)
dataset = load_dataset("gravcal-neurips2026/gravcal", data_dir="sample")

# Access an instance
sample = dataset["train"][0]
image = sample["image"]
gravity_gt = sample["gravity_gt"]
gravity_prior = sample["gravity_prior"]
```

### Evaluation

```python
import numpy as np

def angular_error(pred, gt):
    """Compute angular error in degrees."""
    cos_angle = np.clip(np.dot(pred, gt), -1.0, 1.0)
    return np.arccos(cos_angle) * 180.0 / np.pi

# Evaluate your method
pred_gravity = model(image, gravity_prior)
error = angular_error(pred_gravity, gravity_gt)
```

## Review Sample

For quick inspection, we provide a representative test sequence:

- **Location**: `sample/` directory
- **Size**: ~1.5 GB
- **Frames**: ~1,000 frames
- **Coverage**: Representative distribution of scenes and rotations

Reviewers can download only the sample for quality inspection without waiting for the full 35GB dataset.

## Citation

```bibtex
@inproceedings{gravcal2026,
  title={GravCal: A Large-Scale Orientation-Diverse Dataset and Benchmark for Single-Image IMU Gravity Calibration},
  booktitle={Advances in Neural Information Processing Systems (NeurIPS)},
  year={2026}
}
```

## License

- **Dataset**: CC-BY-4.0
- **Code**: MIT License

## Intended Use

### Primary Uses
- Research in visual-inertial perception
- Gravity estimation and IMU calibration
- Mobile AR and robotics applications
- Sensor fusion algorithm development

### Prohibited Uses
- Commercial surveillance systems
- Biometric identification or tracking
- Any application violating privacy or ethical guidelines

## Contact

For questions, issues, or requests, please open an issue on the [code repository](https://anonymous.4open.science/r/gravcal/).

---

**Status**: Dataset upload in progress. Full dataset will be available by May 6, 2026.

**Version**: 1.0.0  
**Last Updated**: May 2026