Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: RigNet: Repetitive Image Guided Network for Depth Completion   Depth completion deals with the problem of recovering dense depth maps from
sparse ones, where color images are often used to facilitate this task. Recent
approaches mainly focus on image guided learning to predict dense results.
However, blurry guidance in image and unclear structure in depth still impede
the performance of the image guided frameworks. Inspired by the popular
mechanism of looking and thinking twice, we explore a repetitive design in our
image guided network to gradually and sufficiently recover depth values.
Specifically, the repetition is embodied in both the image guidance branch and
depth generation branch. In the former branch, we design a repetitive hourglass
network to extract discriminative image features of complex environments, which
can provide powerful contextual instruction for depth prediction. In the latter
branch, we introduce a repetitive guidance module based on dynamic convolution,
in which an efficient convolution factorization is proposed to simultaneously
reduce its complexity and progressively model high-frequency structures.
Extensive experiments show that our method achieves state-of-the-art results on
the KITTI benchmark and NYUv2 dataset.

26
The categories of this paper are: bayes-an
35
The categories of this paper are: mtrl-th
13
The categories of this paper are: quant-ph
14
The categories of this paper are: math-ph
12
The categories of this paper are: nucl-th
6
The categories of this paper are: astro-ph
30
The categories of this paper are: q-alg
23
The categories of this paper are: solv-int
17
The categories of this paper are: q-fin
18
The categories of this paper are: econ
37
The categories of this paper are: supr-con
4
The categories of this paper are: cond-mat
0
The categories of this paper are: hep-ph
32
The categories of this paper are: atom-ph
8
The categories of this paper are: hep-ex
36
The categories of this paper are: cmp-lg