README.md

---
license: cc-by-nc-4.0
language:
- en
size_categories:
- 10K<n<100K
task_categories:
- image-to-3d
- image-to-image
- object-detection
- keypoint-detection
tags:
- nerf
- aerial
- uav
- 6-dof
- multi-view
- pose-estimation
- neural-rendering
- 3d-reconstruction
- gps
- imu
pretty_name: AeroGrid100
dataset_info:
  title: "AeroGrid100: A Real-World Multi-Pose Aerial Dataset for Implicit Neural Scene Reconstruction"
  authors:
    - Qingyang Zeng
    - Adyasha Mohanty
  paper: "https://im4rob.github.io/attend/papers/7_AeroGrid100_A_Real_World_Mul.pdf"
  workshop: "RSS 2025 Workshop on Leveraging Implicit Methods in Aerial Autonomy"
  bibtex: |
    @inproceedings{zeng2025aerogrid100,
      title     = {AeroGrid100: A Real-World Multi-Pose Aerial Dataset for Implicit Neural Scene Reconstruction},
      author    = {Zeng, Qingyang and Mohanty, Adyasha},
      booktitle = {RSS Workshop on Leveraging Implicit Methods in Aerial Autonomy},
      year      = {2025},
      url       = {https://im4rob.github.io/attend/papers/7_AeroGrid100_A_Real_World_Mul.pdf}
    }
---

# AeroGrid100

**AeroGrid100** is a large-scale, structured aerial dataset collected via UAV to support 3D neural scene reconstruction tasks such as **NeRF**. It consists of **17,100 high-resolution images** with accurate 6-DoF camera poses, collected over a **10×10 geospatial grid** at **5 altitude levels** and **multi-angle views** per point.

## 🔗 Access

To access the full dataset, [**click here to open the Google Drive folder**](https://drive.google.com/drive/folders/1cUUjdoMNSig2Jw_yRBeELuTF6T8c9e_b?usp=drive_link).

## 🌍 Dataset Overview

- **Platform:** DJI Air 3 drone with wide-angle lens
- **Region:** Urban site in Claremont, California (~0.209 km²)
- **Image Resolution:** 4032 × 2268 (JPEG, 24mm FOV)
- **Total Images:** 17,100
- **Grid Layout:** 10 × 10 spatial points
- **Altitudes:** 20m, 40m, 60m, 80m, 100m
- **Viewpoints per Altitude:** Up to 8 yaw × 5 pitch combinations
- **Pose Metadata:** Provided in JSON (extrinsics, GPS, IMU)

## 📦 What’s Included

- High-resolution aerial images
- Per-image pose metadata in NeRF-compatible OpenGL format
- Full drone flight log
- Scene map and sampling diagrams
- Example reconstruction using NeRF

## 🎯 Key Features

- ✅ Dense and structured spatial-angular coverage  
- ✅ Real-world variability (lighting, pedestrians, cars, vegetation)  
- ✅ Precise pose annotations from onboard GNSS + IMU  
- ✅ Designed for photorealistic NeRF reconstruction and benchmarking  
- ✅ Supports pose estimation, object detection, keypoint detection, and novel view synthesis

## 📊 Use Cases

- Neural Radiance Fields (NeRF)
- View synthesis and novel view generation
- Pose estimation and camera localization
- Multi-view geometry and reconstruction benchmarks
- UAV scene understanding in complex environments

## 📌 Citation

If you use AeroGrid100 in your research, please cite:

```bibtex
@inproceedings{zeng2025aerogrid100,
  title     = {AeroGrid100: A Real-World Multi-Pose Aerial Dataset for Implicit Neural Scene Reconstruction},
  author    = {Zeng, Qingyang and Mohanty, Adyasha},
  booktitle = {RSS Workshop on Leveraging Implicit Methods in Aerial Autonomy},
  year      = {2025},
  url       = {https://im4rob.github.io/attend/papers/7_AeroGrid100_A_Real_World_Mul.pdf}
}