Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Neural Non-Rigid Tracking   We introduce a novel, end-to-end learnable, differentiable non-rigid tracker
that enables state-of-the-art non-rigid reconstruction by a learned robust
optimization. Given two input RGB-D frames of a non-rigidly moving object, we
employ a convolutional neural network to predict dense correspondences and
their confidences. These correspondences are used as constraints in an
as-rigid-as-possible (ARAP) optimization problem. By enabling gradient
back-propagation through the weighted non-linear least squares solver, we are
able to learn correspondences and confidences in an end-to-end manner such that
they are optimal for the task of non-rigid tracking. Under this formulation,
correspondence confidences can be learned via self-supervision, informing a
learned robust optimization, where outliers and wrong correspondences are
automatically down-weighted to enable effective tracking. Compared to
state-of-the-art approaches, our algorithm shows improved reconstruction
performance, while simultaneously achieving 85 times faster correspondence
prediction than comparable deep-learning based methods. We make our code
available.

29
The categories of this paper are: funct-an
20
The categories of this paper are: acc-phys
12
The categories of this paper are: nucl-th
18
The categories of this paper are: econ
15
The categories of this paper are: nucl-ex
13
The categories of this paper are: quant-ph
30
The categories of this paper are: q-alg
27
The categories of this paper are: comp-gas
14
The categories of this paper are: math-ph
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
16
The categories of this paper are: stat
5
The categories of this paper are: gr-qc
32
The categories of this paper are: atom-ph
33
The categories of this paper are: plasm-ph
11
The categories of this paper are: hep-lat