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Visual recognition of materials and their states is essential forunderstanding most aspects of the world, from determining whether food iscooked, metal is rusted, or a chemical reaction has occurred. However, currentimage recognition methods are limited to specific classes and properties andcan't handle the vast number of material states in the world. To address this,we present MatSim: the first dataset and benchmark for computer vision-basedrecognition of similarities and transitions between materials and textures,focusing on identifying any material under any conditions using one or a fewexamples. The dataset contains synthetic and natural images. The syntheticimages were rendered using giant collections of textures, objects, andenvironments generated by computer graphics artists. We use mixtures andgradual transitions between materials to allow the system to learn cases withsmooth transitions between states (like gradually cooked food). We also renderimages with materials inside transparent containers to support beverage andchemistry lab use cases. We use this dataset to train a siamese net thatidentifies the same material in different objects, mixtures, and environments.The descriptor generated by this net can be used to identify the states ofmaterials and their subclasses using a single image. We also present the firstfew-shot material recognition benchmark with images from a wide range offields, including the state of foods and drinks, types of grounds, and manyother use cases. We show that a net trained on the MatSim synthetic datasetoutperforms state-of-the-art models like Clip on the benchmark and alsoachieves good results on other unsupervised material classification tasks.

One-shot recognition of any material anywhere using contrastive learning with physics-based rendering