Neural Implicit Representation for Building Digital Twins of Unknown Articulated Objects
CVPR 2024(2024)
摘要
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
更多查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要