  We introduce RPM-Net, a deep learning-based approach which simultaneously
infers movable parts and hallucinates their motions from a single,
un-segmented, and possibly partial, 3D point cloud shape. RPM-Net is a novel
Recurrent Neural Network (RNN), composed of an encoder-decoder pair with
interleaved Long Short-Term Memory (LSTM) components, which together predict a
temporal sequence of pointwise displacements for the input point cloud. At the
same time, the displacements allow the network to learn movable parts,
resulting in a motion-based shape segmentation. Recursive applications of
RPM-Net on the obtained parts can predict finer-level part motions, resulting
in a hierarchical object segmentation. Furthermore, we develop a separate
network to estimate part mobilities, e.g., per-part motion parameters, from the
segmented motion sequence. Both networks learn deep predictive models from a
training set that exemplifies a variety of mobilities for diverse objects. We
show results of simultaneous motion and part predictions from synthetic and
real scans of 3D objects exhibiting a variety of part mobilities, possibly
involving multiple movable parts.
