MatSense: A Multimodal Dataset and Benchmark for Material Classification

Multimodal material understanding with RGB, NIR, Polar, and Thermal imagery.

Illustration of material samples viewed through multiple sensing modalities

Overview

MatSense is a multimodal dataset and benchmark for material classification. It brings together RGB, NIR, Polar, and Thermal imagery to support research on visual material understanding under complementary sensing conditions.

The benchmark is designed to make it easier to study how different sensing modalities contribute to robust material recognition, while providing a clear foundation for comparable future methods and evaluations.

Multimodal Capture

RGB

Visible-spectrum color and appearance information.

NIR

Near-infrared sensing for complementary material cues.

Polar

Polarimetric information that captures surface behavior.

Thermal

Thermal imagery for temperature-related visual structure.

Downloads

MatSense release package

Dataset files, annotations, benchmark protocol, and baseline resources will be made available here.

Dataset download Annotations Benchmark code

Citation

Citation details will be published together with the official MatSense release.

@misc{matsense,
  title = {MatSense: A Multimodal Dataset and Benchmark for Material Classification},
  note = {Dataset release in preparation}
}