The KITTI Vision Benchmark Suite

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 frameworks to predict dense depth.
However, blurry guidance in the image and unclear
structure in the depth still impede the
performance of the image guided frameworks. To
tackle these problems, 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 c

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@inproceedings{yan2022rignet,
title={RigNet: Repetitive Image Guided Network
for Depth Completion},
author={Yan, Zhiqiang and Wang, Kun and Li,
Xiang and Zhang, Zhenyu and Li, Jun and Yang,
Jian},
booktitle={ECCV},
pages={214--230},
year={2022},
organization={Springer}
}