GaMi: Geometry-Agnostic Material Identification via Cross-Modal Subtractive Disentanglement

Zhiwei Chen; Yijie Li; Yimo Zhang; Shiyun Shao; Yichao Chen; Dian Ding; Liang Wang; Haiwei Wu; Liwei Guo; Jie Yang; Xiaosong Zhang; Yongzhao Zhang

arXiv · 2605.30818

GaMi: Geometry-Agnostic Material Identification via Cross-Modal Subtractive Disentanglement

Zhiwei Chen·Yijie Li·Yimo Zhang·Shiyun Shao·Yichao Chen·Dian Ding·Liang Wang·Haiwei Wu·Liwei Guo·Jie Yang·Xiaosong Zhang·Yongzhao Zhang

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Abstract

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.

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Zhiwei Chen, Yijie Li, Yimo Zhang, Shiyun Shao, Yichao Chen, Dian Ding, Liang Wang, Haiwei Wu, Liwei Guo, Jie Yang, Xiaosong Zhang, Yongzhao Zhang. 2026-05-29. GaMi: Geometry-Agnostic Material Identification via Cross-Modal Subtractive Disentanglement. https://arxiv.org/abs/2605.30818

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