Learning Part Motion of Articulated Objects Using Spatially Continuous
  Neural Implicit Representations
Yushi Du Ruihai Wu Yan Shen Hao Dong
Articulated objects (e.g., doors and drawers) exist everywhere in our life.
Different from rigid objects, articulated objects have higher degrees of
freedom and are rich in geometries, semantics, and part functions. Modeling
different kinds of parts and articulations with nerual networks plays an
essential role in articulated object understanding and manipulation, and will
further benefit 3D vision and robotics communities. To model articulated
objects, most previous works directly encode articulated objects into feature
representations, without specific designs for parts, articulations and part
motions. In this paper, we introduce a novel framework that explicitly
disentangles the part motion of articulated objects by predicting the
transformation matrix of points on the part surface, using spatially continuous
neural implicit representations to model the part motion smoothly in the space.
More importantly, while many methods could only model a certain kind of joint
motion (such as the revolution in the clockwise order), our proposed framework
is generic to different kinds of joint motions in that transformation matrix
can model diverse kinds of joint motions in the space. Quantitative and
qualitative results of experiments over diverse categories of articulated
objects demonstrate the effectiveness of our proposed framework.