--- pretty_name: MatSynth dataset_info: version: 1.0.0 homepage: https://gvecchio.com/matsynth/ paperswithcode_id: matsynth description: >- MatSynth is a large-scale dataset comprising over 4,000 ultra-high resolution PBR materials, all released under permissive licensing. 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: 7443356603 num_examples: 89 - name: train num_bytes: 322271809003.6 num_examples: 5700 download_size: 431757376265 dataset_size: 329715165606.6 configs: - config_name: default data_files: - split: test path: data/test-* - split: train path: data/train-* language: - en size_categories: - 1K

## 🔍 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.

## 📂 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} } ```