Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Watertight Mesh Generation for Physics Simulation
  Applications Using Growing Neural Gas on Noisy Free-Form Object Models   We present a framework to generate watertight mesh representations in an
unsupervised manner from noisy point clouds of complex, heterogeneous objects
with free-form surfaces. The resulting meshes are ready to use in applications
like kinematics and dynamics simulation where watertightness and fast
processing are the main quality criteria. This works with no necessity of user
interaction, mainly by utilizing a modified Growing Neural Gas technique for
surface reconstruction combined with several post-processing steps. In contrast
to existing methods, the proposed framework is able to cope with input point
clouds generated by consumer-grade RGBD sensors and works even if the input
data features large holes, e.g. a missing bottom which was not covered by the
sensor. Additionally, we explain a method to unsupervisedly optimize the
parameters of our framework in order to improve generalization quality and, at
the same time, keep the resulting meshes as coherent as possible to the
original object regarding visual and geometric properties.

21
The categories of this paper are: adap-org
10
The categories of this paper are: q-bio
17
The categories of this paper are: q-fin
7
The categories of this paper are: hep-th
9
The categories of this paper are: nlin
29
The categories of this paper are: funct-an
2
The categories of this paper are: math
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
30
The categories of this paper are: q-alg
0
The categories of this paper are: hep-ph
15
The categories of this paper are: nucl-ex
33
The categories of this paper are: plasm-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
28
The categories of this paper are: alg-geom