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license: mit
pretty_name: MatPredict
task_categories:
- image-to-image
- image-segmentation
tags:
- synthetic-data
- inverse-rendering
- material-segmentation
- material-properties
- pbr
- robotics
- indoor-scenes
---
# MatPredict
MatPredict is a synthetic dataset for material-centric scene understanding. It supports two main tasks:
1. **Inverse rendering**: predict material properties such as albedo, roughness, and metallic maps from RGB images.
2. **Material segmentation**: predict material regions or material classes from RGB images.
The dataset contains rendered object variants with paired RGB images, material property maps, segmentation labels, camera transforms, and metadata.
## Dataset Structure
```text
MatPredict/
material_segmentation_map.yaml
config/
object_disjoint_v1.yaml
variance_disjoint_v1.yaml
<object_name>/
<variant_name>/
images/ # RGB input images
albedo/ # base color targets
ORM/ # packed material map; roughness=G, metallic=B
label/ # material segmentation labels
depth/
normal_mat/
normal_obj/
transforms.json
metadata.json
material_segmentation_map.json
```
## Tasks
### Inverse Rendering
Input:
```text
images/*.png
```
Targets:
```text
albedo/*.png
ORM/*.png
```
### Material Segmentation
Input:
```text
images/*.png
```
Target:
```text
label/*.png
```
## Splits
The dataset includes two split files:
- `config/object_disjoint_v1.yaml`: train, validation, and test sets use disjoint object identities.
- `config/variance_disjoint_v1.yaml`: train, validation, and test sets use disjoint material/rendering variants.
Both split files store relative sample ids in the form:
```text
<object_name>/<variant_name>/<frame_id>
```
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