Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: PR-GCN: A Deep Graph Convolutional Network with Point Refinement for 6D
  Pose Estimation   RGB-D based 6D pose estimation has recently achieved remarkable progress, but
still suffers from two major limitations: (1) ineffective representation of
depth data and (2) insufficient integration of different modalities. This paper
proposes a novel deep learning approach, namely Graph Convolutional Network
with Point Refinement (PR-GCN), to simultaneously address the issues above in a
unified way. It first introduces the Point Refinement Network (PRN) to polish
3D point clouds, recovering missing parts with noise removed. Subsequently, the
Multi-Modal Fusion Graph Convolutional Network (MMF-GCN) is presented to
strengthen RGB-D combination, which captures geometry-aware inter-modality
correlation through local information propagation in the graph convolutional
network. Extensive experiments are conducted on three widely used benchmarks,
and state-of-the-art performance is reached. Besides, it is also shown that the
proposed PRN and MMF-GCN modules are well generalized to other frameworks.

24
The categories of this paper are: dg-ga
33
The categories of this paper are: plasm-ph
7
The categories of this paper are: hep-th
28
The categories of this paper are: alg-geom
6
The categories of this paper are: astro-ph
20
The categories of this paper are: acc-phys
16
The categories of this paper are: stat
36
The categories of this paper are: cmp-lg
26
The categories of this paper are: bayes-an
29
The categories of this paper are: funct-an
11
The categories of this paper are: hep-lat
25
The categories of this paper are: patt-sol
23
The categories of this paper are: solv-int
30
The categories of this paper are: q-alg
9
The categories of this paper are: nlin
12
The categories of this paper are: nucl-th