Datasets:

anonymous-vision-bench
/

vision-benchmark

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:

{
    "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:

In-Domain General: Standard test set
Rotation-Stratified: By prior error (0-10°, 10-30°, 30-60°, >60°)
Scene-Specific: Indoor / Outdoor / Low-light
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

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

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

@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.

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

Version: 1.0.0
Last Updated: May 2026