  Humans rely on their visual and tactile senses to develop a comprehensive 3D
understanding of their physical environment. Recently, there has been a growing
interest in exploring and manipulating objects using data-driven approaches
that utilise high-resolution vision-based tactile sensors. However, 3D shape
reconstruction using tactile sensing has lagged behind visual shape
reconstruction because of limitations in existing techniques, including the
inability to generalise over unseen shapes, the absence of real-world testing,
and limited expressive capacity imposed by discrete representations. To address
these challenges, we propose TouchSDF, a Deep Learning approach for tactile 3D
shape reconstruction that leverages the rich information provided by a
vision-based tactile sensor and the expressivity of the implicit neural
representation DeepSDF. Our technique consists of two components: (1) a
Convolutional Neural Network that maps tactile images into local meshes
representing the surface at the touch location, and (2) an implicit neural
function that predicts a signed distance function to extract the desired 3D
shape. This combination allows TouchSDF to reconstruct smooth and continuous 3D
shapes from tactile inputs in simulation and real-world settings, opening up
research avenues for robust 3D-aware representations and improved multimodal
perception in robotics. Code and supplementary material are available at:
https://touchsdf.github.io/
