Neural Implicit Representation for Building Digital Twins of Unknown
  Articulated Objects
Yijia Weng Bowen Wen Jonathan Tremblay Valts Blukis Dieter Fox Leonidas Guibas Stan Birchfield
We address the problem of building digital twins of unknown articulated
objects from two RGBD scans of the object at different articulation states. We
decompose the problem into two stages, each addressing distinct aspects. Our
method first reconstructs object-level shape at each state, then recovers the
underlying articulation model including part segmentation and joint
articulations that associate the two states. By explicitly modeling point-level
correspondences and exploiting cues from images, 3D reconstructions, and
kinematics, our method yields more accurate and stable results compared to
prior work. It also handles more than one movable part and does not rely on any
object shape or structure priors. Project page:
https://github.com/NVlabs/DigitalTwinArt