Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Mobile-URSONet: an Embeddable Neural Network for Onboard Spacecraft Pose
  Estimation   Spacecraft pose estimation is an essential computer vision application that
can improve the autonomy of in-orbit operations. An ESA/Stanford competition
brought out solutions that seem hardly compatible with the constraints imposed
on spacecraft onboard computers. URSONet is among the best in the competition
for its generalization capabilities but at the cost of a tremendous number of
parameters and high computational complexity. In this paper, we propose
Mobile-URSONet: a spacecraft pose estimation convolutional neural network with
178 times fewer parameters while degrading accuracy by no more than four times
compared to URSONet.

17
The categories of this paper are: q-fin
5
The categories of this paper are: gr-qc
29
The categories of this paper are: funct-an
16
The categories of this paper are: stat
23
The categories of this paper are: solv-int
14
The categories of this paper are: math-ph
32
The categories of this paper are: atom-ph
37
The categories of this paper are: supr-con
27
The categories of this paper are: comp-gas
25
The categories of this paper are: patt-sol
24
The categories of this paper are: dg-ga
22
The categories of this paper are: chao-dyn
31
The categories of this paper are: ao-sci
36
The categories of this paper are: cmp-lg
3
The categories of this paper are: physics
4
The categories of this paper are: cond-mat