Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RPM-Net: Recurrent Prediction of Motion and Parts from Point Cloud   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.

16
The categories of this paper are: stat
8
The categories of this paper are: hep-ex
29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
35
The categories of this paper are: mtrl-th
27
The categories of this paper are: comp-gas
31
The categories of this paper are: ao-sci
10
The categories of this paper are: q-bio
28
The categories of this paper are: alg-geom
21
The categories of this paper are: adap-org
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
5
The categories of this paper are: gr-qc
3
The categories of this paper are: physics