Duplicate from gvecchio/MatSynth

Co-authored-by: Giuseppe Vecchio <gvecchio@users.noreply.huggingface.co>

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+*.pcm filter=lfs diff=lfs merge=lfs -text
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README.md

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+---
+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
+import torchvision.transforms.functional as TF
+from datasets import load_dataset
+from torch.utils.data import DataLoader
+
+# image processing function
+def process_img(x):
+    x = TF.resize(x, (1024, 1024))
+    x = TF.to_tensor(x)
+    return x
+
+# item processing function
+def process_batch(examples):
+    examples["basecolor"] = [process_img(x) for x in examples["basecolor"]]
+    return examples
+
+# load the dataset in streaming mode
+ds = load_dataset(
+    "gvecchio/MatSynth", 
+    streaming = True,
+)
+
+# remove unwanted columns
+ds = ds.remove_columns(["diffuse", "specular", "displacement", "opacity", "blend_mask"])
+# or keep only specified columns
+ds = ds.select_columns(["metadata", "basecolor"])
+
+# shuffle data
+ds = ds.shuffle(buffer_size=100)
+
+# filter data matching a specific criteria, e.g.: only CC0 materials
+ds = ds.filter(lambda x: x["metadata"]["license"] == "CC0")
+# filter out data from Deschaintre et al. 2018
+ds = ds.filter(lambda x: x["metadata"]["source"] != "deschaintre_2020")
+
+# Set up processing
+ds = ds.map(process_batch, batched=True, batch_size=8)
+
+# set format for usage in torch
+ds = ds.with_format("torch")
+
+# iterate over the dataset
+for x in ds:
+    print(x)
+
+```
+
+ ⚠️ **Note**: Streaming can be slow. We strongly suggest to cache data locally. 
+
+## 📜 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}
+}
+```

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