2025
SMCNet: Supervised Surface Material Classification Using mmWave Radar IQ Signals and Complex-valued CNNs
ICASSP 2025accepted
Understanding surface material properties is crucial for enhancing indoor robot perception and indoor digital twinning. However, not all sensor modalities typically employed for this task are capable of reliably capturing detailed surface material characteristics. By analyzing the reflected RF signa…