Elasticity is one of therepresentative parameters that reflect the mechanical properties of soft materials. Detecting the underneath elasticity distribution called elastography is a key step for understanding and interacting with objects. Existing solutions for capturing the interior elasticity distribution typically rely on expensive apparatus. In this work, the dense tactile signal captured by the high-resolution vision-based tactile sensor is introduced as a new modality for reconstructing 3D elasticity distribution. We propose a model-based method, which exploits the tactile maps from active pressing trials for the elastography task. The interior elasticity distribution for non-rigid objects is reconstructed from an inverse physics model. We analyze the credibility of the estimated elasticity distribution obtained from our method. Varying design factors are also discussed. We experiment our method on a set of synthesized 3D models and physical models in robot-assisted scenes. Various experimental results have been gathered, demonstrating the efficacy of our approach in perceiving elasticity distribution.

The neural radiance field (NeRF) is attracting increasing attentions from researchers in various fields. While NeRF has produced visually plausible results and found its potential applications in virtual reality, users are only allowed to rotate the camera to observe the scene represented as NeRF. We study the haptic interaction with NeRF models in this paper to enable the experience of touching objects reconstructed by NeRF. Existing haptic rendering algorithms do not work well for NeRF-represented models because NeRF is often noisy. We propose a stochastic haptic rendering method to deal with the collision response between the haptic proxy and NeRF. We validate our method with complex NeRF models and experimental results show the efficacy of our proposed algorithm.