Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for
  Reconstructing Challenging Surfaces
Mauro Comi Alessio Tonioni Max Yang Jonathan Tremblay Valts Blukis Yijiong Lin Nathan F. Lepora Laurence Aitchison
Touch and vision go hand in hand, mutually enhancing our ability to
understand the world. From a research perspective, the problem of mixing touch
and vision is underexplored and presents interesting challenges. To this end,
we propose Tactile-Informed 3DGS, a novel approach that incorporates touch data
(local depth maps) with multi-view vision data to achieve surface
reconstruction and novel view synthesis. Our method optimises 3D Gaussian
primitives to accurately model the object's geometry at points of contact. By
creating a framework that decreases the transmittance at touch locations, we
achieve a refined surface reconstruction, ensuring a uniformly smooth depth
map. Touch is particularly useful when considering non-Lambertian objects (e.g.
shiny or reflective surfaces) since contemporary methods tend to fail to
reconstruct with fidelity specular highlights. By combining vision and tactile
sensing, we achieve more accurate geometry reconstructions with fewer images
than prior methods. We conduct evaluation on objects with glossy and reflective
surfaces and demonstrate the effectiveness of our approach, offering
significant improvements in reconstruction quality.