The KITTI Vision Benchmark Suite

The limitation of LiDAR (Light Detection And
Ranging) sensor causes the general sparsity of
produced depth measurement. However, the sparse
representation of the world is insufficient for
applications such as 3D reconstruction. Thus,
depth completion is an important computer vision
task in which a synchronized RGB image is commonly
available. In this paper, we propose a deep neural
network to tackle this image guided depth
completion problem. By revisiting the sparsity
invariant convolution and revealing how it can be
used in a novel approach, we propose three mask
aware operations to process, downscale, and fuse
sparse features. These operations explicitly
consider the observation mask of its corresponding
feature map. In addition, the structure of this
network follows a novel scheme in which data from
image and depth domain are processed by these
proposed operations independently. Our proposed
model achieves state-of-the-art performance on the
KITTI depth completion benchmark. Furthermore, it
presents a strong robustness for bearing input
sparsity under different densities and patterns.

epochs=60

@ARTICLE{9138427,
author={L. {Yan} and K. {Liu} and E. {Belyaev}},
journal={IEEE Access},
title={Revisiting Sparsity Invariant Convolution:
A Network for Image Guided Depth Completion},
year={2020},
volume={},
number={},
pages={1-1},}