用局部感知建模触觉图像，实现更精准的软体内部结构重建。
More with LESS -- Local Scene Representations for Tactile Imaging
- 基于局部感受野的递归编码器网格，构建物体中心的触觉表征
- 单包含物训练模型可准确重建多包含、不同尺寸物体
- 支持手持触摸与3D重构，适合医疗和机器人触觉应用
触觉成像旨在通过触觉传感重建软体物体的内部结构，应用于医学诊断和机器人操作。现有自监督学习方法虽表现良好，但依赖全局无结构表征与机器人控制传感，限制了泛化能力与实际应用。本文提出局部触觉表征模型LESS，利用触觉的局部特性，将触觉场景建模为具有局部感受野的递归编码器网格，其状态融合以重建2D或3D内部结构图像。该组合式设计实现强泛化：在单包含假体上训练的模型可准确成像含多个异形包含物的物体。局部结构还支持空间不确定性估计。此外，通过外部位姿追踪与类人按压数据，实现手持触觉成像，并扩展至完整3D重建。
原文摘要 · Abstract (English)
Tactile imaging seeks to reconstruct the internal structure of soft objects through touch sensing, with applications in medical diagnosis and robotic manipulation. Recent self-supervised learning approaches have shown promising results, but rely on global, unstructured representations and robot-controlled sensing, limiting generalization and practical use. We propose Local Encoder for Spatial Sensing (LESS), an object-centric tactile representation that exploits the local nature of touch. The tactile scene is modeled as a grid of recurrent encoders with local receptive fields, whose states are fused to reconstruct 2D or 3D images of internal structure. This compositional design enables strong generalization: models trained on single-inclusion phantoms accurately image objects with multiple inclusions and varying sizes. The local structure further supports spatial uncertainty estimation. In addition, we enable hand-held tactile imaging via external pose tracking and human-like palpation data, and extend tactile imaging to full 3D reconstruction.
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