  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.
