Datasets:
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---
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}
}
|