Rectified Iterative Disparity for Stereo Matching

TL;DR

This paper introduces a cost volume-based uncertainty estimation method and two uncertainty-assisted disparity refinement techniques, significantly improving stereo matching accuracy with low computational overhead.

Contribution

It proposes a novel cost volume-based uncertainty estimation and integrates it into an iterative stereo matching framework without extra parameters.

Findings

Achieves competitive disparity estimation performance on multiple datasets.

Improves accuracy of small disparity updates with a new loss function.

Demonstrates effectiveness of uncertainty-assisted methods in stereo matching.

Abstract

Both uncertainty-assisted and iteration-based methods have achieved great success in stereo matching. However, existing uncertainty estimation methods take a single image and the corresponding disparity as input, which imposes higher demands on the estimation network. In this paper, we propose Cost volume-based disparity Uncertainty Estimation (UEC). Based on the rich similarity information in the cost volume coming from the image pairs, the proposed UEC can achieve competitive performance with low computational cost. Secondly, we propose two methods of uncertainty-assisted disparity estimation, Uncertainty-based Disparity Rectification (UDR) and Uncertainty-based Disparity update Conditioning (UDC). These two methods optimise the disparity update process of the iterative-based approach without adding extra parameters. In addition, we propose Disparity Rectification loss that significantly improves the accuracy of small amount of disparity updates. We present a high-performance stereo architecture, DR Stereo, which is a combination of the proposed methods. Experimental results from SceneFlow, KITTI, Middlebury 2014, and ETH3D show that DR-Stereo achieves very competitive disparity estimation performance.