Proximity3D: Shape from Capacitive Proximity on Sensing Manifold
2026-08-31 • Computer Vision and Pattern Recognition
Computer Vision and Pattern RecognitionComputational GeometryGraphicsRobotics
AI summaryⓘ
The authors worked with a special type of fabric that can sense shapes by measuring how close objects are to its curved surface. Unlike usual methods that use flat images, their sensor is curved and captures a 3D proximity field. They developed a model that combines multiple views from this sensor to figure out the shape of nearby objects. Their tests show this method works well on curved surfaces, suggesting a new way for robots to understand shapes up close using flexible sensors.
shape reconstructioncapacitive sensingcurved sensing surfacemanifoldproximity fieldfeedforward modelmulti-view aggregationrobotic sensingembodied sensinggeometric awareness
Authors
Hao Chen, Chenming Wu, Chun Ping Lam, Xiangjia Chen, Guoxin Fang, Charlie C. L. Wang, Yeung Yam, Juncong Lin, Chengkai Dai
Abstract
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.