MatSim Dataset and benchmark for one-shot visual materials and textures recognition

The MatSim Dataset and benchmark

Synthetic dataset and real images benchmark for visual similarity recognition of materials and textures.

MatSim: a synthetic dataset, a benchmark, and a method for computer vision-based recognition of similarities and transitions between materials and textures focusing on identifying any material under any conditions using one or a few examples (one-shot learning).

Based on the paper: One-shot recognition of any material anywhere using contrastive learning with physics-based rendering

Benchmark_MATSIM.zip:  contain the benchmark made of real-world images as described in the paper

Dataset Generation Scripts.zip: Contain the Blender (4.1) Python scripts used for generating the dataset

MatSim_object_train_split_1,2,3....zip: Contain a subset of the synthetics dataset for images of CGI images materials on random objects as described in the paper.

MatSimTrainObjectsNearField_.zip Contain train sets with near fieldlight sources

MatSim_Vessels_Train_1,2,3....zip : Contain a subset of the synthetics dataset for images of CGI images materials inside transparent containers as described in the paper.

*Note: these are subsets of the dataset; the full dataset can be found at:
https://e1.pcloud.link/publink/show?code=kZIiSQZCYU5M4HOvnQykql9jxF4h0KiC5MX

or
https://icedrive.net/s/A13FWzZ8V2aP9T4ufGQ1N3fBZxDF