Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Descriptor Networks for 3D Shape Synthesis and Analysis   This paper proposes a 3D shape descriptor network, which is a deep
convolutional energy-based model, for modeling volumetric shape patterns. The
maximum likelihood training of the model follows an "analysis by synthesis"
scheme and can be interpreted as a mode seeking and mode shifting process. The
model can synthesize 3D shape patterns by sampling from the probability
distribution via MCMC such as Langevin dynamics. The model can be used to train
a 3D generator network via MCMC teaching. The conditional version of the 3D
shape descriptor net can be used for 3D object recovery and 3D object
super-resolution. Experiments demonstrate that the proposed model can generate
realistic 3D shape patterns and can be useful for 3D shape analysis.

34
The categories of this paper are: chem-ph
11
The categories of this paper are: hep-lat
17
The categories of this paper are: q-fin
19
The categories of this paper are: eess
14
The categories of this paper are: math-ph
25
The categories of this paper are: patt-sol
15
The categories of this paper are: nucl-ex
37
The categories of this paper are: supr-con
10
The categories of this paper are: q-bio
6
The categories of this paper are: astro-ph
2
The categories of this paper are: math
12
The categories of this paper are: nucl-th
24
The categories of this paper are: dg-ga
28
The categories of this paper are: alg-geom
7
The categories of this paper are: hep-th
31
The categories of this paper are: ao-sci