Toward Ubiquitous 3D Object Digitization: A Wearable Computing Framework for Non-Invasive Physical Property Acquisition

TL;DR

This paper introduces a wearable, non-invasive framework for estimating physical properties like elasticity and internal pressure of deformable objects through finger touches, enhancing digital object representation.

Contribution

It presents a novel wearable system that non-invasively measures physical properties of deformable objects, enabling more realistic digital replication of their behaviors.

Findings

Pressure estimation error of 3.5%

Deformation discrepancy less than 10.1%

Effective generalization to irregular shapes

Abstract

Accurately digitizing physical objects is central to many applications, including virtual/augmented reality, industrial design, and e-commerce. Prior research has demonstrated efficient and faithful reconstruction of objects' geometric shapes and visual appearances, which suffice for digitally representing rigid objects. In comparison, physical properties, such as elasticity and pressure, are also indispensable to the behavioral fidelity of digitized deformable objects. However, existing approaches to acquiring these quantities either rely on invasive specimen collection or expensive/bulky laboratory setups, making them inapplicable to consumer-level usage. To fill this gap, we propose a wearable and non-invasive computing framework that allows users to conveniently estimate the material elasticity and internal pressure of deformable objects through finger touches. This is achieved by modeling their local surfaces as pressurized elastic shells and analytically deriving the two physical properties from finger-induced wrinkling patterns. Together with photogrammetry-reconstructed geometry and textures, the two estimated physical properties enable us to faithfully replicate the motion and deformation behaviors of several deformable objects. For the pressure estimation, our model achieves a relative error of 3.5%. In the interaction experiments, the virtual-physical deformation discrepancy measures less than 10.1%. Generalization to objects of irregular shape further demonstrates the potential of our approach in practical applications. We envision this work to provide insights for and motivate research toward democratizing the ubiquitous and pervasive digitization of our physical surroundings in daily, industrial, and scientific scenarios.