| --- |
| 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. |
