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README.md

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-license: mit
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+# AgriPCD: A Dataset for Stationary-Camera-Based 3D Reconstruction Using Neural Radiance Fields
+
+[**📄 Paper Link**](https://arxiv.org/abs/2503.21958)
+
+---
+
+## 🧾 Overview
+
+This dataset complements our paper on a stationary-camera-based NeRF framework for high-throughput 3D plant phenotyping. Traditional NeRF pipelines require a moving camera around a static object, which is impractical in automated indoor phenotyping environments. Our method enables 3D point cloud (PCD) reconstruction using a stationary camera and a rotating object, significantly simplifying the imaging setup.
+
+The **AgriPCD** dataset includes videos, extracted frames, COLMAP pose estimates, NeRF training outputs, and final 3D point clouds. It supports the community in replicating, extending, or evaluating methods for indoor phenotyping under controlled imaging conditions with minimal hardware requirements.
+
+---
+
+## 📁 Dataset Directory Structure
+AgriPCD/ 
+├── raw/ # Raw videos and extracted frames 
+├── pre/ # Preprocessed COLMAP outputs and sparse point clouds 
+├── train/ # Trained NeRF models and checkpoints 
+└── pcd/ # Final reconstructed point clouds (10M points)
+
+### `raw/`
+Contains high-resolution video clips (`.MOV`) and extracted keyframes for six objects:
+- Apricot
+- Banana
+- Corn
+- Paprika
+- Plant-1
+- Plant-2
+
+Each object includes two recording modes:
+- `_sc`: stationary camera and rotating object *(target configuration)*
+- `_gt`: moving camera and stationary object *(used as ground truth)*
+
+### `pre/`
+Includes COLMAP pose estimations and sparse point clouds generated using the `ns-process-data` pipeline in [Nerfstudio](https://docs.nerf.studio). This folder transforms stationary camera captures into NeRF-compatible input by simulating camera motion.
+
+### `train/`
+Stores trained NeRF models for each object using the `nerfacto` trainer in Nerfstudio.
+
+### `pcd/`
+Final 10M-point cloud representations of each object, filtered, scaled using metric references, and aligned with ground-truth models for evaluation.
+
+---
+
+## 🌿 Object Categories
+
+The dataset covers six agriculturally relevant objects with varying geometric complexity:
+
+- **Apricot**
+- **Banana**
+- **Bell Pepper (Paprika)**
+- **Maize Ear (Corn Cob)**
+- **Crassula ovata (Potted Plant 1)**
+- **Haworthia sp. (Potted Plant 2)**
+
+Each object is reconstructed under two experimental protocols:
+
+- `SC` – Stationary Camera, Rotating Object (Primary Contribution)
+- `GT` – Ground Truth: Moving Camera, Stationary Object (Reference)
+
+---
+
+## 🎥 Imaging Protocol
+
+- Captured using an **iPhone 13 Mini** at 4K, 30fps  
+- Videos trimmed to 30s, keyframes extracted at 4–5 FPS  
+- **ArUco markers** and a **ping pong ball (Ø = 0.04 m)** used for pose and scale calibration  
+- Processed using **COLMAP** and **Nerfstudio**
+
+---
+
+## 🧪 Applications
+
+- High-throughput plant phenotyping  
+- AI training for 3D reconstruction  
+- Point cloud alignment and evaluation  
+- Hyperspectral and multimodal NeRF fusion  
+
+---
+
+## 📜 License
+
+**CC-BY-NC-4.0**  
+[Creative Commons Attribution-NonCommercial 4.0 International License](https://creativecommons.org/licenses/by-nc/4.0/)
+
+---
+## 🔖 Citation
+If you use this dataset in your work, please cite:
+
+@article{ku2025stationarynerf,
+  title = {NeRF-based Point Cloud Reconstruction using a Stationary Camera for Agricultural Applications},
+  author = {Kibon Ku, Talukder Z. Jubery, Elijah Rodriguez, Aditya Balu, Soumik Sarkar, Adarsh Krishnamurthy, Baskar Ganapathysubramanian},
+  year = {2025},
+  journal = {arXiv preprint arXiv:2503.21958}
+}
+