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

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:

images/*.png

Targets:

albedo/*.png
ORM/*.png

Material Segmentation

Input:

images/*.png

Target:

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:

<object_name>/<variant_name>/<frame_id>