# Unsupervised monocular stereo matching

## TL;DR

This paper introduces an unsupervised monocular stereo matching approach using two deep networks, outperforming existing unsupervised depth estimation methods on the KITTI dataset by reconstructing views and estimating depth.

## Contribution

It presents a novel unsupervised monocular stereo matching method with dual deep networks, improving depth estimation accuracy without ground truth labels.

## Key findings

- Outperforms current mainstream unsupervised methods on KITTI dataset
- Uses two pipelined deep convolution networks for view reconstruction and depth estimation
- Demonstrates the effectiveness of stereo matching models over monocular models in unsupervised learning

## Abstract

At present, deep learning has been applied more and more in monocular image depth estimation and has shown promising results. The current more ideal method for monocular depth estimation is the supervised learning based on ground truth depth, but this method requires an abundance of expensive ground truth depth as the supervised labels. Therefore, researchers began to work on unsupervised depth estimation methods. Although the accuracy of unsupervised depth estimation method is still lower than that of supervised method, it is a promising research direction. In this paper, Based on the experimental results that the stereo matching models outperforms monocular depth estimation models under the same unsupervised depth estimation model, we proposed an unsupervised monocular vision stereo matching method. In order to achieve the monocular stereo matching, we constructed two unsupervised deep convolution network models, one was to reconstruct the right view from the left view, and the other was to estimate the depth map using the reconstructed right view and the original left view. The two network models are piped together during the test phase. The output results of this method outperforms the current mainstream unsupervised depth estimation method in the challenging KITTI dataset.