Update README.md

README.md

@@ -38,3 +38,60 @@ dataset_info:
     }
 ---
 
+# 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}
+}
+
+