Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for
  Reconstructing Challenging Surfaces
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.
660a2a29e29bac7275f565db
Mauro Comi
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2025-06-09T10:15:10.912Z
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Mauro Comi
user
MauroC
660a2a29e29bac7275f565dc
Alessio Tonioni
660a2a29e29bac7275f565dd
Max Yang
660a2a29e29bac7275f565de
Jonathan Tremblay
660a2a29e29bac7275f565df
Valts Blukis
660a2a29e29bac7275f565e0
Yijiong Lin
660a2a29e29bac7275f565e1
Nathan F. Lepora
660a2a29e29bac7275f565e2
Laurence Aitchison
Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for
  Reconstructing Challenging Surfaces
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.
A novel approach combining touch and vision data improves surface reconstruction and novel view synthesis, especially for non-Lambertian objects, using optimized 3D Gaussian primitives.
3D Gaussian primitives