发表机构
Centre for Perceptual and Interactive Intelligence; Xiamen University; The Chinese University of Hong Kong; The University of Manchester（感知与交互智能中心; 厦门大学; 香港中文大学; 曼彻斯特大学）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
Proximity3D提出将弯曲电容织物表面作为非平面感知流形，引入多视图前馈重建模型，通过聚合电容邻近场实现物体形状鲁棒重建，为机器人近场几何感知提供新路径。
AI 中文摘要
大多数形状重建方法假设测量是在平面感知域上定义的，例如RGB图像或深度图。本文中，我们使用弯曲的电容织物作为形状传感器，将其表面视为非平面感知流形。每次扫描表示为该流形上的电容邻近场，由弯曲电极布局与附近物体几何结构的相互作用诱导产生。我们引入一种多视图前馈重建模型，该模型聚合已知传感器视图下的这些场并恢复被观测物体的形状。模拟实验和物理实验均证明，从弯曲感知表面采集的电容邻近信号可实现鲁棒的重建，为机器人通过具身感知实现近场几何感知开辟了新途径。
英文摘要
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.
