Create dataset_card.yaml

dataset_card.yaml

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+Dataset Metadata
+
+dataset_info:
+  name: AgriPCD
+  description: >
+    AgriPCD is a dataset designed for 3D reconstruction using Neural Radiance Fields (NeRF) under a 
+    stationary-camera setup. It targets high-throughput plant phenotyping in controlled indoor environments, 
+    simplifying the traditional NeRF pipeline that requires a moving camera around static objects. 
+    Instead, it uses a rotating object in front of a stationary camera, making it practical for automated 
+    phenotyping systems. The dataset includes videos, extracted frames, COLMAP pose estimations, trained NeRF 
+    models, and high-resolution point clouds for six agriculturally relevant objects.
+
+  version: 1.0
+  license: CC-BY-NC-4.0
+  authors:
+    - Kibon Ku
+    - Talukder Z. Jubery
+    - Elijah Rodriguez
+    - Aditya Balu
+    - Soumik Sarkar
+    - Adarsh Krishnamurthy
+    - Baskar Ganapathysubramanian
+
+  citation: >
+    @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}
+    }
+
+  intended_use:
+    - Stationary-camera-based 3D reconstruction
+    - High-throughput plant phenotyping
+    - AI-based point cloud generation
+    - Benchmarking indoor NeRF pipelines
+    - Hyperspectral and multimodal NeRF fusion
+
+  features:
+    - Videos (.MOV)
+    - Keyframes (JPG/PNG)
+    - COLMAP outputs (poses, sparse PCD)
+    - Trained NeRF models (nerfacto, Nerfstudio format)
+    - Final reconstructed 10M-point point clouds (.ply)
+
+  dataset_size:
+    raw:
+      - "6 video objects × 2 capture types (SC and GT) in .MOV format"
+      - "Keyframes extracted at 4–5 FPS per object"
+    pre:
+      - "COLMAP pose estimates and sparse point clouds for all objects"
+    train:
+      - "Nerfstudio-trained NeRF models with checkpoints"
+    pcd:
+      - "Final 10M-point point clouds for 6 objects (SC and GT), aligned and filtered"
+
+  dependencies:
+    - Python 3.8+
+    - Nerfstudio (https://docs.nerf.studio)
+    - COLMAP
+    - Open3D (for visualization and evaluation)
+    - CloudCompare or MeshLab (optional for inspection)
+
+  installation_instructions: |
+    Clone and set up the dataset locally:
+    ```bash
+    git clone https://huggingface.co/datasets/BGLab/AgriPCD
+    cd AgriPCD
+    ```
+
+  download_instructions: |
+    1. Download the dataset files from the Hugging Face repository or provided links.
+    2. Unzip the folders:
+       ```bash
+       unzip raw.zip
+       unzip pre.zip
+       unzip train.zip
+       unzip pcd.zip
+       ```
+
+  training_instructions: |
+    Preprocess and train NeRF models using Nerfstudio:
+    ```bash
+    ns-process-data --data ./pre/object_name
+    ns-train nerfacto --data ./pre/object_name
+    ```
+
+  pointcloud_extraction: |
+    Export the high-resolution point cloud:
+    ```bash
+    ns-export pointcloud --load-config ./train/object_name/config.yml
+    ```
+
+  evaluation_instructions: |
+    Align reconstructed and ground truth point clouds using ICP and evaluate using precision/recall or other geometric metrics.
+
+  visualization_instructions: |
+    Visualize point clouds using Open3D:
+    ```python
+    import open3d as o3d
+    pcd = o3d.io.read_point_cloud("pcd/apricot_sc.ply")
+    o3d.visualization.draw_geometries([pcd])
+    ```
+
+  repository_links:
+    - https://huggingface.co/datasets/BGLab/AgriPCD
+    - https://arxiv.org/abs/2503.21958
+    - https://docs.nerf.studio
+