The KITTI Vision Benchmark Suite

Extracting dense depth from a single image is an
important and challenging task in computer vision.
Recently, significant progress has been made in
monocular depth estimation (MDE) by formulating
this problem as a multi-label classification
instead of traditional regression. However, to
discretize depth into hundreds of labels results
in complicate network output and a large number of
parameters. In this paper, we propose to encode
the discretized depth into a binary code and
formulate MDE as a hierarchical binary
classification (HBC) problem to reduce the
complexity. Besides, by studying the distribution
of individual bit of the encoded depth codes, we
find that our encoding scheme can also solve the
problem of depth data imbalance. We conduct
experiments on KITTI and NYU Depth V2 datasets,
which show that our simplified approach still
achieves a comparable performance with state-of-
the-art methods.

learning rate = 10e-4

@inproceedings{jiang2019hbc,
title={Hierarchical Binary Classification
for Monocular Depth Estimation},
author={Hualie Jiang and Rui Huang},
booktitle={IEEE International Conference on
Robotics and Biomimetics},
year={2019}
}