Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Unsupervised Metric Relocalization Using Transform Consistency Loss   Training networks to perform metric relocalization traditionally requires
accurate image correspondences. In practice, these are obtained by restricting
domain coverage, employing additional sensors, or capturing large multi-view
datasets. We instead propose a self-supervised solution, which exploits a key
insight: localizing a query image within a map should yield the same absolute
pose, regardless of the reference image used for registration. Guided by this
intuition, we derive a novel transform consistency loss. Using this loss
function, we train a deep neural network to infer dense feature and saliency
maps to perform robust metric relocalization in dynamic environments. We
evaluate our framework on synthetic and real-world data, showing our approach
outperforms other supervised methods when a limited amount of ground-truth
information is available.

14
The categories of this paper are: math-ph
10
The categories of this paper are: q-bio
31
The categories of this paper are: ao-sci
24
The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
36
The categories of this paper are: cmp-lg
7
The categories of this paper are: hep-th
30
The categories of this paper are: q-alg
19
The categories of this paper are: eess
20
The categories of this paper are: acc-phys
33
The categories of this paper are: plasm-ph
8
The categories of this paper are: hep-ex
26
The categories of this paper are: bayes-an
6
The categories of this paper are: astro-ph
22
The categories of this paper are: chao-dyn
21
The categories of this paper are: adap-org