Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions

• URL: http://arxiv.org/abs/2604.11579v1
• Date: Mon, 13 Apr 2026 14:57:52 GMT
• Title: Seeing Through Touch: Tactile-Driven Visual Localization of Material Regions
• Abstract summary: We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input.<n>Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task.<n>We propose a model that learns local visuo-tactile alignment via dense cross-modal feature interactions, producing tactile saliency maps for touch-conditioned material segmentation.
• Score: 42.26043073031702
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: We address the problem of tactile localization, where the goal is to identify image regions that share the same material properties as a tactile input. Existing visuo-tactile methods rely on global alignment and thus fail to capture the fine-grained local correspondences required for this task. The challenge is amplified by existing datasets, which predominantly contain close-up, low-diversity images. We propose a model that learns local visuo-tactile alignment via dense cross-modal feature interactions, producing tactile saliency maps for touch-conditioned material segmentation. To overcome dataset constraints, we introduce: (i) in-the-wild multi-material scene images that expand visual diversity, and (ii) a material-diversity pairing strategy that aligns each tactile sample with visually varied yet tactilely consistent images, improving contextual localization and robustness to weak signals. We also construct two new tactile-grounded material segmentation datasets for quantitative evaluation. Experiments on both new and existing benchmarks show that our approach substantially outperforms prior visuo-tactile methods in tactile localization.

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