Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Y-GAN: A Generative Adversarial Network for Depthmap Estimation from
  Multi-camera Stereo Images   Depth perception is a key component for autonomous systems that interact in
the real world, such as delivery robots, warehouse robots, and self-driving
cars. Tasks in autonomous robotics such as 3D object recognition, simultaneous
localization and mapping (SLAM), path planning and navigation, require some
form of 3D spatial information. Depth perception is a long-standing research
problem in computer vision and robotics and has had a long history. Many
approaches using deep learning, ranging from structure from motion,
shape-from-X, monocular, binocular, and multi-view stereo, have yielded
acceptable results. However, there are several shortcomings of these methods
such as requiring expensive hardware, needing supervised training data, no
ground truth data for comparison, and disregard for occlusion. In order to
address these shortcomings, this work proposes a new deep convolutional
generative adversarial network architecture, called Y-GAN, that uses data from
three cameras to estimate a depth map for each frame in a multi-camera video
stream.

15
The categories of this paper are: nucl-ex
19
The categories of this paper are: eess
27
The categories of this paper are: comp-gas
8
The categories of this paper are: hep-ex
34
The categories of this paper are: chem-ph
28
The categories of this paper are: alg-geom
13
The categories of this paper are: quant-ph
24
The categories of this paper are: dg-ga
3
The categories of this paper are: physics
30
The categories of this paper are: q-alg
5
The categories of this paper are: gr-qc
35
The categories of this paper are: mtrl-th
4
The categories of this paper are: cond-mat
22
The categories of this paper are: chao-dyn
17
The categories of this paper are: q-fin
31
The categories of this paper are: ao-sci