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---
license: mit
task_categories:
- image-segmentation
- image-to-3d
pretty_name: nespof
library_name:
- nerfstudio
tags:
- nerf
- hyperspectral
- material-segmentation
- 3d
- robotics
- augmented-reality
- simulation
---
# Extended NeSpoF Dataset
<div align="center">
**[Fabian Perez](https://github.com/Factral)¹² · [Sara Rojas](https://sararoma95.github.io/sr/)² · [Carlos Hinojosa](https://carloshinojosa.me/)² · [Hoover Rueda-Chacón](http://hfarueda.com/)¹ · [Bernard Ghanem](https://www.bernardghanem.com/)²**
¹Universidad Industrial de Santander · ²King Abdullah University of Science and Technology (KAUST)
</div>
## Introduction
This dataset is an extension of the NeSpoF dataset, enriched with ground-truth material labels for evaluating material segmentation in synthetic multi-view settings. The annotations provide consistent material labeling across different viewpoints for comprehensive scene analysis.
It is used in conjunction with **UnMix-NeRF**, a framework presented in the paper [UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields](https://huggingface.co/papers/2506.21884). UnMix-NeRF integrates spectral unmixing into Neural Radiance Fields (NeRF), enabling joint hyperspectral novel view synthesis and unsupervised material segmentation.
### Dataset Sources
* **Github:** [Official Code](https://github.com/Factral/UnMix-NeRF)
* **Paper:** [UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields (ICCV 2025)](https://arxiv.org/pdf/2506.21884)
* **Project Page:** [UnMix-NeRF Project Page](https://www.factral.co/UnMix-NeRF)
* **Repository:** [Original NeSpoF Repository](https://github.com/youngchan-k/nespof)
## Direct Use
This dataset is intended for training and evaluating models for material segmentation tasks, particularly useful for multi-view segmentation scenarios and NeRF-based material analysis.
## Dataset Structure
The dataset has the following directory structure:
```
scene/
├── color/
│ ├── eval/
│ └── train/
│ └── r_x.png
└── raw/
├── eval/
└── train/
└── r_x.png
```
Here, `x` corresponds to the matching frame ID from the original NeSpoF dataset.
## Dataset Creation
### Source Data
#### Who are the source data producers?
The dataset extension was produced by the authors of the paper "UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields," accepted at ICCV 2025.
### Annotations
#### Annotation process
Annotations were automatically generated by rendering the ground-truth material indices, corresponding consistently across views and matching original scene frames.
#### Who are the annotators?
Automated rendering processed by mitsuba 3.
## Bias, Risks, and Limitations
No known biases or risks are identified in this synthetic dataset. However, its synthetic nature may limit direct applicability to real-world scenarios without additional adaptation or fine-tuning.
### Recommendations
Users should be aware that performance on this synthetic dataset may not fully generalize to real-world data without further adaptation.
## Citation
If you use this dataset, please cite the following paper:
```bibtex
@inproceedings{perez2025unmix,
title={UnMix-NeRF: Spectral Unmixing Meets Neural Radiance Fields},
author={Perez, Fabian and Rojas, Sara and Hinojosa, Carlos and Rueda-Chac{\'o}n, Hoover and Ghanem, Bernard},
booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision},
year={2025}
}
```
## Dataset Card Contact
For inquiries regarding the dataset, please contact the corresponding authors listed in the referenced paper. |