GaMi: Geometry-Agnostic Material Identification via Cross-Modal Subtractive Disentanglement
This system addresses the challenge of robust material identification for embodied intelligence by mitigating geometric variations, which is an incremental improvement for robotics and human-computer interaction.
This paper introduces GaMi, a multimodal material identification system using mmWave and acoustic sensing to overcome geometry-induced variations. It achieves 95.2% accuracy across 20 materials and unseen geometric conditions, outperforming single-modality baselines.
Non-contact material identification enables adaptive interaction for embodied intelligence yet faces challenges from geometry-induced variations (e.g., orientation, shape, distance) and single-modality ambiguities. In this paper, we present GaMi, a multimodal material identification system integrating mmWave and acoustic sensing to robustly operate under unconstrained geometric conditions. By leveraging the insight of shared geometric consistency between co-located bimodal sensors, GaMi employs an intra-sample cross-modal subtractive disentanglement framework. By semantically aligning modalities and subtracting the shared geometric context, it isolates intrinsic material features. Furthermore, GaMi incorporates inter-sample contrastive learning to correct the residual interference caused by cross-modal misalignment. Additionally, a pairing-based adaptation strategy between two modalities enables few-shot generalization across devices. Extensive evaluations on 20 materials show that GaMi achieves 95.2% accuracy, outperforming single-modality baselines across unseen geometric conditions.