ECT-Mat: An Experimental Mutual Capacitance Dataset for Material Sensing and Inverse Problem Benchmarking

The dataset contains 54,739 high-resolution mutual capacitance matrices acquired via a custom 3×3 electrode array. Taking advantage of a uniffed robotic pipeline, data was collected under comprehensive spatial variables, covering contact and multi-distance non-contact conditions (Z-axis permutations), alongside continuous spatio-temporal trajectories.

Three representative materials (glass, resin, wood) and geometric shapes (circle, triangle, square) are thoroughly documented. Each sample couples raw capacitance signals with strict positional metadata and ground-truth object labels. Furthermore, ECT-Bench provides quantitative reconstructions comparing traditional physical solvers (Linear Back Projection, Landweber) against data-driven deep learning (CNNs).