|
--- |
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language: |
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- en |
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size_categories: |
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- 1K<n<10K |
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task_categories: |
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- image-to-image |
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- unconditional-image-generation |
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- image-classification |
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- text-to-image |
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pretty_name: MatSynth |
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dataset_info: |
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features: |
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- name: name |
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dtype: string |
|
- name: category |
|
dtype: |
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class_label: |
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names: |
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'0': ceramic |
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'1': concrete |
|
'2': fabric |
|
'3': ground |
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'4': leather |
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'5': marble |
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'6': metal |
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'7': misc |
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'8': plaster |
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'9': plastic |
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'10': stone |
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'11': terracotta |
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'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 |
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configs: |
|
- config_name: default |
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data_files: |
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- split: test |
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path: data/test-* |
|
- split: train |
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path: data/train-* |
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tags: |
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- materials |
|
- pbr |
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- 4d |
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- graphics |
|
- rendering |
|
- svbrdf |
|
- synthetic |
|
viewer: false |
|
--- |
|
|
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# MatSynth |
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|
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MatSynth is a Physically Based Rendering (PBR) materials dataset designed for modern AI applications. |
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This dataset consists of over 4,000 ultra-high resolution, offering unparalleled scale, diversity, and detail. |
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|
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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. |
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|
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For further information, refer to our paper: ["MatSynth: A Modern PBR Materials Dataset"](https://arxiv.org/abs/2401.06056) available on arXiv. |
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|
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<center> |
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<img src="https://gvecchio.com/matsynth/static/images/teaser.png" style="border-radius:10px"> |
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</center> |
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|
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## 🔍 Dataset Details |
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|
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### Dataset Description |
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|
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MatSynth is a new large-scale dataset comprising over 4,000 ultra-high resolution Physically Based Rendering (PBR) materials, |
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all released under permissive licensing. |
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|
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All materials in the dataset are represented by a common set of maps (*Basecolor*, *Diffuse*, *Normal*, *Height*, *Roughness*, *Metallic*, *Specular* and, when useful, *Opacity*), |
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modelling both the reflectance and mesostructure of the material. |
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|
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Each material in the dataset comes with rich metadata, including information on its origin, licensing details, category, tags, creation method, |
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and, when available, descriptions and physical size. |
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This comprehensive metadata facilitates precise material selection and usage, catering to the specific needs of users. |
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|
|
<center> |
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<img src="https://gvecchio.com/matsynth/static/images/data.png" style="border-radius:10px"> |
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</center> |
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|
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## 📂 Dataset Structure |
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|
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The MatSynth dataset is divided into two splits: the test split, containing 89 materials, and the train split, consisting of 3,980 materials. |
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|
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## 🔨 Dataset Creation |
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|
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The MatSynth dataset is designed to support modern, learning-based techniques for a variety of material-related tasks including, |
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but not limited to, material acquisition, material generation and synthetic data generation e.g. for retrieval or segmentation. |
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|
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### 🗃️ Source Data |
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|
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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. |
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This collection strategy allows to capture a broad spectrum of materials, |
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from commonly used ones to more niche or specialized variants while guaranteeing that the data can be used for a variety of usecases. |
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|
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Materials under CC0 license were collected from [AmbientCG](https://ambientcg.com/), [CGBookCase](https://www.cgbookcase.com/), [PolyHeaven](https://polyhaven.com/), |
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[ShateTexture](https://www.sharetextures.com/), and [TextureCan](https://www.texturecan.com/). |
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The dataset also includes limited set of materials from the artist [Julio Sillet](https://juliosillet.gumroad.com/), distributed under CC-BY license. |
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|
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We collected over 6000 materials which we meticulously filter to keep only tileable, 4K materials. |
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This high resolution allows us to extract many different crops from each sample at different scale for augmentation. |
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Additionally, we discard blurry or low-quality materials (by visual inspection). |
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The resulting dataset consists of 3736 unique materials which we augment by blending semantically compatible materials (e.g.: snow over ground). |
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In total, our dataset contains 4069 unique 4K materials. |
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|
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### ✒️ Annotations |
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|
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The dataset is composed of material maps (Basecolor, Diffuse, Normal, Height, Roughness, Metallic, Specular and, when useful, opacity) |
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and associated renderings under varying environmental illuminations, and multi-scale crops. |
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We adopt the OpenGL standard for the Normal map (Y-axis pointing upward). |
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The Height map is given in a 16-bit single channel format for higher precision. |
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|
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In addition to these maps, the dataset includes other annotations providing context to each material: |
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the capture method (photogrammetry, procedural generation, or approximation); |
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list of descriptive tags; source name (website); source link; |
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licensing and a timestamps for eventual future versioning. |
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For a subset of materials, when the information is available, we also provide the author name (387), text description (572) and a physical size, |
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presented as the length of the edge in centimeters (358). |
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|
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## 🧑💻 Usage |
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|
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MatSynth is accessible through the datasets python library. |
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Following a usage example: |
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|
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```python |
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import torchvision.transforms.functional as TF |
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from datasets import load_dataset |
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from torch.utils.data import DataLoader |
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|
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# image processing function |
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def process_img(x): |
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x = TF.resize(x, (1024, 1024)) |
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x = TF.to_tensor(x) |
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return x |
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|
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# item processing function |
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def process_batch(examples): |
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examples["basecolor"] = [process_img(x) for x in examples["basecolor"]] |
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return examples |
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|
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# load the dataset in streaming mode |
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ds = load_dataset( |
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"gvecchio/MatSynth", |
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streaming = True, |
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) |
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|
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# remove unwanted columns |
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ds = ds.remove_columns(["diffuse", "specular", "displacement", "opacity", "blend_mask"]) |
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# or keep only specified columns |
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ds = ds.select_columns(["metadata", "basecolor"]) |
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|
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# shuffle data |
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ds = ds.shuffle(buffer_size=100) |
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|
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# filter data matching a specific criteria, e.g.: only CC0 materials |
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ds = ds.filter(lambda x: x["metadata"]["license"] == "CC0") |
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# filter out data from Deschaintre et al. 2018 |
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ds = ds.filter(lambda x: x["metadata"]["source"] != "deschaintre_2020") |
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|
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# Set up processing |
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ds = ds.map(process_batch, batched=True, batch_size=8) |
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|
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# set format for usage in torch |
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ds = ds.with_format("torch") |
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|
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# iterate over the dataset |
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for x in ds: |
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print(x) |
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|
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``` |
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|
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⚠️ **Note**: Streaming can be slow. We strongly suggest to cache data locally. |
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|
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## 📜 Citation |
|
|
|
``` |
|
@inproceedings{vecchio2023matsynth, |
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title={MatSynth: A Modern PBR Materials Dataset}, |
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author={Vecchio, Giuseppe and Deschaintre, Valentin}, |
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booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition}, |
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year={2024} |
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} |
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``` |
|
|
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If you use the data from Deschaintre et al. contained in this dataset, please also cite: |
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``` |
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@article{deschaintre2018single, |
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title={Single-image svbrdf capture with a rendering-aware deep network}, |
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author={Deschaintre, Valentin and Aittala, Miika and Durand, Fredo and Drettakis, George and Bousseau, Adrien}, |
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journal={ACM Transactions on Graphics (ToG)}, |
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volume={37}, |
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number={4}, |
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pages={1--15}, |
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year={2018}, |
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publisher={ACM New York, NY, USA} |
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} |
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``` |
