Snap-it, Tap-it, Splat-it: Tactile-Informed 3D Gaussian Splatting for Reconstructing Challenging Surfaces

Mauro Comi, Alessio Tonioni, Jonathan Tremblay, Max Yang, Valts Blukis, Yijiong Lin, Nathan F. Lepora, Laurence Aitchison

Keywords: 3D reconstruction, robotic manipulation, tactile sensing

TL;DR: A novel method for 3D shape reconstruction of challenging surfaces using 3D Gaussian Splatting and robotic tactile sensing.

Abstract: 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 together is underexplored and presents interesting challenges. To this end, we propose Tactile-Informed 3DGS, a novel approach that incorporates contact data (local depth maps) with multi-view images 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 often to fail to reconstruct such objects with fidelity. By combining vision and tactile sensing, we achieve more accurate geometry reconstructions with fewer images than prior methods. We conduct evaluation in both the virtual and real world on objects with glossy and reflective surfaces to demonstrate the effectiveness of our approach in improving reconstruction quality.

Submission Number: 362