Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning elementary structures for 3D shape generation and matching   We propose to represent shapes as the deformation and combination of
learnable elementary 3D structures, which are primitives resulting from
training over a collection of shape. We demonstrate that the learned elementary
3D structures lead to clear improvements in 3D shape generation and matching.
More precisely, we present two complementary approaches for learning elementary
structures: (i) patch deformation learning and (ii) point translation learning.
Both approaches can be extended to abstract structures of higher dimensions for
improved results. We evaluate our method on two tasks: reconstructing ShapeNet
objects and estimating dense correspondences between human scans (FAUST inter
challenge). We show 16% improvement over surface deformation approaches for
shape reconstruction and outperform FAUST inter challenge state of the art by
6%.

7
The categories of this paper are: hep-th
18
The categories of this paper are: econ
10
The categories of this paper are: q-bio
14
The categories of this paper are: math-ph
0
The categories of this paper are: hep-ph
37
The categories of this paper are: supr-con
36
The categories of this paper are: cmp-lg
4
The categories of this paper are: cond-mat
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
9
The categories of this paper are: nlin
5
The categories of this paper are: gr-qc
24
The categories of this paper are: dg-ga
19
The categories of this paper are: eess
16
The categories of this paper are: stat
13
The categories of this paper are: quant-ph