Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Deep Learning for Structured Shape Matching   We present a novel method for computing correspondences across 3D shapes
using unsupervised learning. Our method computes a non-linear transformation of
given descriptor functions, while optimizing for global structural properties
of the resulting maps, such as their bijectivity or approximate isometry. To
this end, we use the functional maps framework, and build upon the recent FMNet
architecture for descriptor learning. Unlike that approach, however, we show
that learning can be done in a purely \emph{unsupervised setting}, without
having access to any ground truth correspondences. This results in a very
general shape matching method that we call SURFMNet for Spectral Unsupervised
FMNet, and which can be used to establish correspondences within 3D shape
collections without any prior information. We demonstrate on a wide range of
challenging benchmarks, that our approach leads to state-of-the-art results
compared to the existing unsupervised methods and achieves results that are
comparable even to the supervised learning techniques. Moreover, our framework
is an order of magnitude faster, and does not rely on geodesic distance
computation or expensive post-processing.

21
The categories of this paper are: adap-org
8
The categories of this paper are: hep-ex
24
The categories of this paper are: dg-ga
17
The categories of this paper are: q-fin
11
The categories of this paper are: hep-lat
13
The categories of this paper are: quant-ph
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
29
The categories of this paper are: funct-an
0
The categories of this paper are: hep-ph
18
The categories of this paper are: econ
20
The categories of this paper are: acc-phys
36
The categories of this paper are: cmp-lg
35
The categories of this paper are: mtrl-th
9
The categories of this paper are: nlin
10
The categories of this paper are: q-bio