Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Learning Monocular Visual Odometry through Geometry-Aware Curriculum
  Learning   Inspired by the cognitive process of humans and animals, Curriculum Learning
(CL) trains a model by gradually increasing the difficulty of the training
data. In this paper, we study whether CL can be applied to complex geometry
problems like estimating monocular Visual Odometry (VO). Unlike existing CL
approaches, we present a novel CL strategy for learning the geometry of
monocular VO by gradually making the learning objective more difficult during
training. To this end, we propose a novel geometry-aware objective function by
jointly optimizing relative and composite transformations over small windows
via bounded pose regression loss. A cascade optical flow network followed by
recurrent network with a differentiable windowed composition layer, termed
CL-VO, is devised to learn the proposed objective. Evaluation on three
real-world datasets shows superior performance of CL-VO over state-of-the-art
feature-based and learning-based VO.

15
The categories of this paper are: nucl-ex
29
The categories of this paper are: funct-an
21
The categories of this paper are: adap-org
26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
24
The categories of this paper are: dg-ga
25
The categories of this paper are: patt-sol
37
The categories of this paper are: supr-con
8
The categories of this paper are: hep-ex
7
The categories of this paper are: hep-th
17
The categories of this paper are: q-fin
34
The categories of this paper are: chem-ph
14
The categories of this paper are: math-ph
30
The categories of this paper are: q-alg
4
The categories of this paper are: cond-mat
5
The categories of this paper are: gr-qc