| --- |
| license: cc0-1.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - Zero-shot |
| - One-Shot |
| - Material-Segmentation |
| - Soft-Segmentation |
| --- |
| |
| # MatSeg Dataset |
|
|
| The MatSeg dataset focuses on **zero-shot segmentation of materials and their states** identifying image regions belonging to a specific material type or state, without any prior training on that material, state, or environment. |
|
|
| It emphasizes complex, scattered, and sparse material boundaries, as well as soft similarity and gradual transitions between materials. |
|
|
| **For Example:** |
| - Regions belonging to different materials |
| - Regions belong to the same material but in different states: |
| - Wet regions on surfaces |
| - Scattered dust |
| - Minerals in rocks |
| - Sediment in soils |
| - Rotten parts of fruits |
| - Degraded and corrosive surface regions |
|
|
|
|
| --- |
|
|
| ## Paper |
|
|
| This dataset is introduced and described in: |
| [Infusing Synthetic Data with Real-World Patterns for |
| Zero-Shot Material State Segmentation](https://proceedings.neurips.cc/paper_files/paper/2024/file/6ef4a4b387a5a547ea699f3df7fc1248-Paper-Datasets_and_Benchmarks_Track.pdf) |
|
|
|
|
| ## Training Dataset |
|
|
| Composed of **100,000 images**, the synthetic dataset including **3D rendered scenes** with realistic illumination and PBR materials as well as **2D maps**. |
| With textures boundaries extracted from real-world images and infused into synthetic data |
| --- |
|
|
| # Real-World Benchmark |
|
|
| The benchmark contains **1,220 annotated real-world images** for evaluation and testing across a diverse set of material states and environments. |
|
|
| It focuses on: |
|
|
| - **Zero-shot material segmentation (segmenting region of the image belonging to the same material without for any material type)** |
| - Separation between **different states of the same material** |
| - Soft segmentation and partial similarity (For example regions if A and B are same material but in different states like wet/dry) |
| - Segmentation of materials with **complex and scattered boundaries** |
|
|
| For Example: |
|
|
| - Food states and spoilage |
| - Plant diseases and infections |
| - Rocks and minerals |
| - Soil and sediment |
| - Corrosion and rust |
| - Liquids and foam |
| - Worn or degraded surfaces |
| - Diverse unconstrained environment |
|
|
|
|
|
|
|
|
| ## File Structure |
|
|
| | File | Description | |
| |---|---| |
| | `Dataset_Documentation_And_Readers.zip` | Dataset readers and technical documentation | |
| | `MatSeg_Benchmark.zip` | Real-world evaluation benchmark | |
| | `MatSeg2D_part_*.zip` | 2D synthetic training set | |
| | `MatSeg3D_part_*.zip` | 3D synthetic training set | |
|
|
|
|
| ## Alternative Soruces: |
|
|
| [Main Website](https://sites.google.com/d/1MYugs4Pqam5bSjqVpYfJKL8wENnCYDIa/p/1ho8QyUQ_uJQXzImyslBMS63qxe8qr5TS/edit) |
|
|
| [Zenodo](https://zenodo.org/records/11331618) |
|
|
