---
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

![UnMix-NeRF Overview](https://i.imgur.com/D3SaEU8.png)

<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.