Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning models for visual 3D localization with implicit mapping   We consider learning based methods for visual localization that do not
require the construction of explicit maps in the form of point clouds or
voxels. The goal is to learn an implicit representation of the environment at a
higher, more abstract level. We propose to use a generative approach based on
Generative Query Networks (GQNs, Eslami et al. 2018), asking the following
questions: 1) Can GQN capture more complex scenes than those it was originally
demonstrated on? 2) Can GQN be used for localization in those scenes? To study
this approach we consider procedurally generated Minecraft worlds, for which we
can generate images of complex 3D scenes along with camera pose coordinates. We
first show that GQNs, enhanced with a novel attention mechanism can capture the
structure of 3D scenes in Minecraft, as evidenced by their samples. We then
apply the models to the localization problem, comparing the results to a
discriminative baseline, and comparing the ways each approach captures the task
uncertainty.

2
The categories of this paper are: math
21
The categories of this paper are: adap-org
7
The categories of this paper are: hep-th
0
The categories of this paper are: hep-ph
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
24
The categories of this paper are: dg-ga
4
The categories of this paper are: cond-mat
28
The categories of this paper are: alg-geom
37
The categories of this paper are: supr-con
11
The categories of this paper are: hep-lat
18
The categories of this paper are: econ
13
The categories of this paper are: quant-ph
35
The categories of this paper are: mtrl-th
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg