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Title: UNSUPERVISED MONOCULAR DEPTH ESTIMATION WITH CLEAR BOUNDARIES

Paper content:
Unsupervised monocular depth estimation has made great progress after deep
learning is involved.
Training with binocular stereo images is considered as a
good option as the data can be easily obtained.
However, the depth or disparity
prediction results show poor performance for the object boundaries.
The main
reason is related to the handling of occlusion areas during the training.
In this paper,
we propose a novel method to overcome this issue.
Exploiting disparity maps
property, we generate an occlusion mask to block the back-propagation of the occlusion
areas during image warping.
We also design new networks with flipped
stereo images to induce the networks to learn occluded boundaries.
It shows that
our method achieves clearer boundaries and better evaluation results on KITTI
driving dataset and Virtual KITTI dataset.
Monocular depth estimation becomes an active research topic as deep learning is applied in various computer vision tasks.
It has many applications, from navigation through to scene understanding.
A single traditional camera can be a cheaper alternative to the expensive LIDAR sensor for automotive cars if accurate estimation can be achieved.
Meanwhile, single camera simplifies the design of depth estimation solution which can be adopted quite widely at a low cost.
One straight-forward way to train deep depth estimation models is to use ground truth depth images as the supervision signals BID1 .
However, supervised deep learning method is eager for massive data with ground truth.
Collecting large datasets with ground truth depth in varied real scenarios is challenge and expensive.
Instead, training using stereo images without depth label is an alternative option.
BID7 proposed a method to exploit the left-right consistency of stereo images to tackle the monocular depth estimation, which achieved quite promising results.
However, the depth predicted by their method has blurred boundaries.
The issue is mainly due to the occlusions during the image warping.
Though it can be alleviated in some extent with proper post processing, the fundamental problem is not well addressed.In this paper, we propose a new method to overcome the blurred boundaries when using stereo pairs to train the monocular depth model.
An example is illustrated in FIG0 .
During the image warping, we generate an occlusion mask using the disparity map to block the inappropriate back-propagation gradients for occlusion areas.
However, the mask only cannot guarantee clear boundaries as there is no constrain for the masked areas.
Then we design new networks to fully exploit the information of stereo images.
With flipped stereo pairs, the network is induced to learn clear boundaries for occlusion areas.
Our method provides a solution to the fundamental learning difficulty of occluded areas introduced by image warping in depth estimation.
Empirical evaluation on KITTI driving dataset BID6 ) and Virtual KITTI dataset BID4 ) demonstrates the effectiveness of our approach.
Moreover, we find the depth label of KITTI 2015 is usually very sparse near the object boundaries, which is not very sensitive to evaluate the clearness of boundaries.
In this work, we present an occlusion mask and filp-over training scheme to enable effective learning of object boundaries when using image warping.
With our new network, our model achieves state of art result using only stereo images.
Moreover, as warping based image reconstruction is commonly used in depth estimation problem, our method provides a solution to the fundamental difficulty of occluded areas introduced by image warping.In the future, our method can be incorporated with more accurate network trained on trinocular data (temporal Stereo sequence) such as BID25 , BID17 and BID8 , which would further boost the accuracy.6
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assistant: text: This paper propose a mask method which solves the previous blurred results of unsupervised monocular depth estimation caused by occlusion