Create README.md

README.md

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+---
+license: mit
+task_categories:
+- image-segmentation
+pretty_name: nespof
+---
+
+
+# Extended NeSpoF Dataset
+
+<div align="center">
+  <img src="https://i.imgur.com/D3SaEU8.png" alt="UnMix-NeRF Overview" width="40%">
+
+  <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>
+
+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.
+
+</div>
+
+
+### 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)
+* **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.