HSI-SC-NeRF: NeRF-based Hyperspectral 3D Reconstruction using a Stationary Camera for Agricultural Applications

📄 Paper Link

🧾 Overview

This dataset accompanies our work on HSI-SC-NeRF, a stationary-camera-based hyperspectral NeRF framework for 3D plant phenotyping and postharvest agricultural inspection. Unlike conventional NeRF pipelines that require camera motion around a static object, our approach uses a fixed camera and a rotating object, making the acquisition setup simpler and more practical for controlled imaging. The system is built around a custom PTFE (Teflon) studio chamber designed to provide diffuse and spatially uniform illumination for hyperspectral acquisition. After white-reference-based spectral calibration and COLMAP-based pose estimation, a multi-channel NeRF jointly reconstructs all spectral bands to produce high-fidelity 3D hyperspectral point clouds that preserve both geometric structure and spectral reflectance information.

The dataset includes white-reference data for spectral calibration, raw hyperspectral acquisitions, an iPhone-captured video for spatial validation, pseudo-RGB images for pose estimation, COLMAP camera poses, masks for fine-tuning, two-stage NeRF training outputs, evaluation metric JSON files and per-band error maps for spectral validation, and final hyperspectral 3D point clouds. It is intended to support reproducible research in hyperspectral 3D reconstruction, spectral-geometric scene representation, and automated agricultural inspection under controlled imaging conditions.

✨ Key Contributions

• Stationary-camera NeRF for 3D hyperspectral reconstruction without requiring camera movement
• A custom PTFE studio chamber for diffuse and spatially uniform hyperspectral illumination
• A multi-channel NeRF formulation that jointly reconstructs all hyperspectral bands
• High-fidelity 3D hyperspectral point clouds through joint spectral-geometric reconstruction
• Validation for automated, high-throughput postharvest agricultural inspection

📁 Dataset Directory Structure

For readability, the directory tree below is shown in a simplified logical form. Actual released folder names may retain some original pipeline-specific naming for compatibility with preprocessing and training scripts.

HSI-SC-NeRF/
├── wr/                              # White reference (WR) data for spectral calibration
├── raw/                             # Raw acquisition data
│   ├── apple_bruised/
│   ├── maize/
│   │   ├── Oh43x_gt.MOV             # iPhone-captured video for spatial validation
│   │   └── Oh43x_SC/                # Raw hyperspectral acquisition data
│   └── pear/
├── processed/                       # Preprocessed and NeRF-ready data
│   ├── apple_bruised/
│   │   ├── pseudo_rgb/              # Pseudo-RGB images for pose estimation
│   │   ├── colmap/                  # COLMAP outputs and sparse point clouds
│   │   ├── hsi_calibrated/          # Spectrally calibrated hyperspectral data
│   │   └── masks/                   # Object masks for fine-tuning
│   ├── maize/                       
│   └── pear/                        
├── train/                           # Trained NeRF models and checkpoints
│   ├── apple_bruised/
│   │   ├── pre-train/               # Pre-trained model checkpoints (`.ckpt`)
│   │   ├── fine-tune/               # Fine-tuned model checkpoints (`.ckpt`)
│   │   ├── eval_metrics/            # Evaluation metrics for pre-trained and fine-tuned models
│   │   └── eval_maps/               # Per-band RMSE or error maps (`.npy`)
│   ├── maize/                       
│   └── pear/                       
└── pcd/                             # Final reconstructed hyperspectral point clouds (1M points)
    ├── apple_bruised/
    │   ├── pre-train/
    │   └── fine-tune/
    ├── maize/                       
    └── pear/                        

🧠 HSI-SC-NeRF Pipeline

The reconstruction workflow consists of three main stages:

1. Dataset Acquisition Experimental setup and multi-view hyperspectral image collection using a stationary camera and a rotating object.

2. Data Preprocessing White-reference spectral calibration, pseudo-RGB generation, and COLMAP-based pose estimation.

3. NeRF-Based Hyperspectral Point Cloud Reconstruction Multi-channel hyperspectral NeRF training, hyperspectral point cloud generation, and refinement.

This pipeline produces final 3D hyperspectral point clouds that support downstream spatial and spectral analysis.

🌱 Applications

• Hyperspectral 3D reconstruction
• Plant phenotyping
• Postharvest agricultural inspection
• Spectral-geometric representation learning
• Benchmarking stationary-camera NeRF pipelines under controlled imaging conditions

📜 License

CC-BY-NC-4.0
Creative Commons Attribution-NonCommercial 4.0 International License

🔖 Citation

If you use this dataset in your work, please cite:

@article{ku2026hyperstationarynerf,
title = {HSI-SC-NeRF: NeRF-based Hyperspectral 3D Reconstruction using a Stationary Camera for Agricultural Applications},
author = {Kibon Ku, Talukder Z. Jubery, Adarsh Krishnamurthy, Baskar Ganapathysubramanian},
year = {2026},
journal = {arXiv preprint arXiv:2602.16950}
}