用弯曲电容织物感知物体形状，实现非平面传感下的三维重建。
Proximity3D: Shape from Capacitive Proximity on Sensing Manifold
- 将弯曲电容织物视为非平面传感流形，捕捉近场电容信号。
- 多视角前馈模型融合电容场数据，实现高鲁棒性三维重建。
- 适合机器人近场几何感知与柔性传感器应用。
多数形状重建方法依赖于平面传感域上的测量，如RGB图像或深度图。本文使用弯曲电容织物作为形状传感器，将其表面视为非平面传感流形。每次扫描在该流形上表现为由曲面电极布局与附近物体几何形状相互作用产生的电容接近场。我们引入一种多视角前馈重建模型，跨已知传感器视角聚合这些场，以恢复观测物体的形状。仿真与物理实验均证明，基于弯曲传感表面获取的电容接近信号可实现稳健的三维重建，为机器人近场几何感知提供了新的具身传感路径。
原文摘要 · Abstract (English)
Most shape reconstruction methods assume measurements defined over planar sensing domains, such as RGB images or depth maps. In this paper, we use a curved capacitive textile as a shape sensor, treating its surface as a non-planar sensing manifold. Each scan is represented as a capacitive proximity field on this manifold, induced by the interaction between the curved electrode layout and nearby object geometry. We introduce a multi-view feedforward reconstruction model that aggregates these fields across known sensor views and recovers the observed object shape. Simulated and physical experiments demonstrate robust reconstruction from capacitive proximity signals acquired on curved sensing surfaces, pointing toward a new route to robotic near-field geometric awareness via embodied sensing.
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