Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: ActiveStereoNet: End-to-End Self-Supervised Learning for Active Stereo
  Systems   In this paper we present ActiveStereoNet, the first deep learning solution
for active stereo systems. Due to the lack of ground truth, our method is fully
self-supervised, yet it produces precise depth with a subpixel precision of
$1/30th$ of a pixel; it does not suffer from the common over-smoothing issues;
it preserves the edges; and it explicitly handles occlusions. We introduce a
novel reconstruction loss that is more robust to noise and texture-less
patches, and is invariant to illumination changes. The proposed loss is
optimized using a window-based cost aggregation with an adaptive support weight
scheme. This cost aggregation is edge-preserving and smooths the loss function,
which is key to allow the network to reach compelling results. Finally we show
how the task of predicting invalid regions, such as occlusions, can be trained
end-to-end without ground-truth. This component is crucial to reduce blur and
particularly improves predictions along depth discontinuities. Extensive
quantitatively and qualitatively evaluations on real and synthetic data
demonstrate state of the art results in many challenging scenes.

34
The categories of this paper are: chem-ph
7
The categories of this paper are: hep-th
16
The categories of this paper are: stat
24
The categories of this paper are: dg-ga
9
The categories of this paper are: nlin
6
The categories of this paper are: astro-ph
29
The categories of this paper are: funct-an
12
The categories of this paper are: nucl-th
30
The categories of this paper are: q-alg
36
The categories of this paper are: cmp-lg
14
The categories of this paper are: math-ph
17
The categories of this paper are: q-fin
26
The categories of this paper are: bayes-an
20
The categories of this paper are: acc-phys
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex