MatSim-Dataset

The MatSim dataset includes large-scale collections of synthetic images for material self-similarity and a diverse natural image benchmark (Figure 1) to test the ability of the net to identify material states and subclasses using one or a few examples. This dataset is designed to address the general issue of one-shot material retrieval without restrictions on material types, settings, and environments. The main focus is on distinguishing between states of materials and identifying fine-grained categories such as rotten vs. ripe or coffee vs. cocoa. Additionally, we created a second adversarial benchmark to test the net's ability to recognize materials without association with objects or environments. This benchmark involves covering objects with random materials to create uncorrelated material-object associations.

{Paper}, {Video} , {Poster}

Paper

Detailed on the dataset could be found in the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

Download links:

Dataset and benchmark download links: pcloud, icedrive, zenodo

Dataset Generation scripts

Synthetic dataset generation script (blender):

[1) Materials on random objects generation script

[2) Materials inside transparent containers generation script

Benchmark Examples:

Benchmark 1) Real-world scenes: materials states, gradual transitions, and fine-grained classes:

Benchmark 2) Uncorrelated materials and objects (Random materials on random objects):

Synthethic dataset Examples:

Materials on objects gradual transitions:

Materials in transparent vessels gradual transitions:

Supporting code:

Example neural net trained on the dataset