| --- |
| language: |
| - en |
| size_categories: |
| - 1K<n<10K |
| task_categories: |
| - image-to-image |
| - unconditional-image-generation |
| - image-classification |
| - text-to-image |
| pretty_name: MatSynth |
| dataset_info: |
| features: |
| - name: name |
| dtype: string |
| - name: category |
| dtype: |
| class_label: |
| names: |
| '0': ceramic |
| '1': concrete |
| '2': fabric |
| '3': ground |
| '4': leather |
| '5': marble |
| '6': metal |
| '7': misc |
| '8': plaster |
| '9': plastic |
| '10': stone |
| '11': terracotta |
| '12': wood |
| - name: metadata |
| struct: |
| - name: authors |
| sequence: string |
| - name: category |
| dtype: string |
| - name: description |
| dtype: string |
| - name: height_factor |
| dtype: float32 |
| - name: height_mean |
| dtype: float32 |
| - name: license |
| dtype: string |
| - name: link |
| dtype: string |
| - name: maps |
| sequence: string |
| - name: method |
| dtype: string |
| - name: name |
| dtype: string |
| - name: physical_size |
| dtype: float32 |
| - name: source |
| dtype: string |
| - name: stationary |
| dtype: bool |
| - name: tags |
| sequence: string |
| - name: version_date |
| dtype: string |
| - name: basecolor |
| dtype: image |
| - name: diffuse |
| dtype: image |
| - name: displacement |
| dtype: image |
| - name: height |
| dtype: image |
| - name: metallic |
| dtype: image |
| - name: normal |
| dtype: image |
| - name: opacity |
| dtype: image |
| - name: roughness |
| dtype: image |
| - name: specular |
| dtype: image |
| - name: blend_mask |
| dtype: image |
| splits: |
| - name: test |
| num_bytes: 7443356066.0 |
| num_examples: 89 |
| - name: train |
| num_bytes: 430581667965.1 |
| num_examples: 5700 |
| download_size: 440284274332 |
| dataset_size: 438025024031.1 |
| configs: |
| - config_name: default |
| data_files: |
| - split: test |
| path: data/test-* |
| - split: train |
| path: data/train-* |
| tags: |
| - materials |
| - pbr |
| - 4d |
| - graphics |
| - rendering |
| - svbrdf |
| - synthetic |
| viewer: false |
| --- |
| |
| # MatSynth |
|
|
| MatSynth is a Physically Based Rendering (PBR) materials dataset designed for modern AI applications. |
| This dataset consists of over 4,000 ultra-high resolution, offering unparalleled scale, diversity, and detail. |
|
|
| Meticulously collected and curated, MatSynth is poised to drive innovation in material acquisition and generation applications, providing a rich resource for researchers, developers, and enthusiasts in computer graphics and related fields. |
|
|
| For further information, refer to our paper: ["MatSynth: A Modern PBR Materials Dataset"](https://arxiv.org/abs/2401.06056) available on arXiv. |
|
|
| <center> |
| <img src="https://gvecchio.com/matsynth/static/images/teaser.png" style="border-radius:10px"> |
| </center> |
|
|
| ## 🔍 Dataset Details |
|
|
| ### Dataset Description |
|
|
| MatSynth is a new large-scale dataset comprising over 4,000 ultra-high resolution Physically Based Rendering (PBR) materials, |
| all released under permissive licensing. |
|
|
| All materials in the dataset are represented by a common set of maps (*Basecolor*, *Diffuse*, *Normal*, *Height*, *Roughness*, *Metallic*, *Specular* and, when useful, *Opacity*), |
| modelling both the reflectance and mesostructure of the material. |
|
|
| Each material in the dataset comes with rich metadata, including information on its origin, licensing details, category, tags, creation method, |
| and, when available, descriptions and physical size. |
| This comprehensive metadata facilitates precise material selection and usage, catering to the specific needs of users. |
|
|
| <center> |
| <img src="https://gvecchio.com/matsynth/static/images/data.png" style="border-radius:10px"> |
| </center> |
|
|
| ## 📂 Dataset Structure |
| |
| The MatSynth dataset is divided into two splits: the test split, containing 89 materials, and the train split, consisting of 3,980 materials. |
|
|
| ## 🔨 Dataset Creation |
|
|
| The MatSynth dataset is designed to support modern, learning-based techniques for a variety of material-related tasks including, |
| but not limited to, material acquisition, material generation and synthetic data generation e.g. for retrieval or segmentation. |
|
|
| ### 🗃️ Source Data |
|
|
| The MatSynth dataset is the result of an extensively collection of data from multiple online sources operating under the CC0 and CC-BY licensing framework. |
| This collection strategy allows to capture a broad spectrum of materials, |
| from commonly used ones to more niche or specialized variants while guaranteeing that the data can be used for a variety of usecases. |
|
|
| Materials under CC0 license were collected from [AmbientCG](https://ambientcg.com/), [CGBookCase](https://www.cgbookcase.com/), [PolyHeaven](https://polyhaven.com/), |
| [ShateTexture](https://www.sharetextures.com/), and [TextureCan](https://www.texturecan.com/). |
| The dataset also includes limited set of materials from the artist [Julio Sillet](https://juliosillet.gumroad.com/), distributed under CC-BY license. |
|
|
| We collected over 6000 materials which we meticulously filter to keep only tileable, 4K materials. |
| This high resolution allows us to extract many different crops from each sample at different scale for augmentation. |
| Additionally, we discard blurry or low-quality materials (by visual inspection). |
| The resulting dataset consists of 3736 unique materials which we augment by blending semantically compatible materials (e.g.: snow over ground). |
| In total, our dataset contains 4069 unique 4K materials. |
|
|
| ### ✒️ Annotations |
|
|
| The dataset is composed of material maps (Basecolor, Diffuse, Normal, Height, Roughness, Metallic, Specular and, when useful, opacity) |
| and associated renderings under varying environmental illuminations, and multi-scale crops. |
| We adopt the OpenGL standard for the Normal map (Y-axis pointing upward). |
| The Height map is given in a 16-bit single channel format for higher precision. |
|
|
| In addition to these maps, the dataset includes other annotations providing context to each material: |
| the capture method (photogrammetry, procedural generation, or approximation); |
| list of descriptive tags; source name (website); source link; |
| licensing and a timestamps for eventual future versioning. |
| For a subset of materials, when the information is available, we also provide the author name (387), text description (572) and a physical size, |
| presented as the length of the edge in centimeters (358). |
|
|
| ## 🧑💻 Usage |
|
|
| MatSynth is accessible through the datasets python library. |
| Following a usage example: |
|
|
| ```python |
| from datasets import load_dataset |
| from torch.utils.data import DataLoader |
| |
| # load the dataset in streaming mode |
| ds = load_dataset( |
| "gvecchio/MatSynth", |
| streaming = True, |
| ) |
| |
| # remove unnecessary columns to reduce downloaded data |
| ds = ds.remove_columns(["diffuse", "specular", "displacement", "opacity", "blend_mask"]) |
| # keep only specified columns |
| ds = ds.select_columns(["metadata", "basecolor", "normal", "roughness", "metallic"]) |
| |
| # filter data matching a specific criteria, e.g.: only CC0 materials |
| ds = ds.filter(lambda x: x["metadata"]["license"] == "CC0") |
| |
| # shuffle data |
| ds = ds.shuffle(buffer_size=100) |
| |
| # set format for usage in torch |
| ds = ds.with_format("torch") |
| dl = DataLoader(ds["test"], batch_size=8) |
| ``` |
|
|
| ## 📜 Citation |
|
|
| ``` |
| @inproceedings{vecchio2023matsynth, |
| title={MatSynth: A Modern PBR Materials Dataset}, |
| author={Vecchio, Giuseppe and Deschaintre, Valentin}, |
| booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
| year={2024} |
| } |
| ``` |
|
|
| If you use the data from Deschaintre et al. contained in this dataset, please also cite: |
| ``` |
| @article{deschaintre2018single, |
| title={Single-image svbrdf capture with a rendering-aware deep network}, |
| author={Deschaintre, Valentin and Aittala, Miika and Durand, Fredo and Drettakis, George and Bousseau, Adrien}, |
| journal={ACM Transactions on Graphics (ToG)}, |
| volume={37}, |
| number={4}, |
| pages={1--15}, |
| year={2018}, |
| publisher={ACM New York, NY, USA} |
| } |
| ``` |
