Abstract

Recent stereo matching methods, especially end-to-end deep stereo matching networks, have achieved remarkable performance in the fields of autonomous driving and depth sensing. However, state-of-the-art stereo algorithms, even with the deep neural network framework, still have difficulties at finding correct correspondences in near-range regions and object edge cues. To reinforce the precision of disparity prediction, in the present study, we propose a parallax attention stereo matching algorithm based on the improved group-wise correlation stereo network to learn the disparity content from a stereo correspondence, and it supports end-to-end predictions of both disparity map and edge map. Particular, we advocate for a parallax attention module in three-dimensional (disparity, height and width) level, which structure ensures high-precision estimation by improving feature expression in near-range regions. This is critical for computer vision tasks and can be utilized in several existing models to enhance their performance. Moreover, in order to making full use of the edge information learned by two-dimensional feature extraction network, we propose a novel edge detection branch and multi-featured integration cost volume. It is demonstrated that based on our model, edge detection project is conducive to improve the accuracy of disparity estimation. Our method achieves better results than previous works on both Scene Flow and KITTI datasets.

Highlights

  • The binocular stereo matching task is an imperative, but difficult scientific problem, which aims at computing disparity data for every pixel from a stereo correspondence

  • parallax attention (PA)-Net is the first to emphasize that by improving feature expression in near-range regions is helpful to disparity prediction task

  • It is demonstrated that based on our model, edge detection project is conducive to improve the accuracy of disparity estimation

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Summary

Introduction

The binocular stereo matching task is an imperative, but difficult scientific problem, which aims at computing disparity data for every pixel from a stereo correspondence. Efficient and correct stereo matching methods are necessary for computer vision tasks such as robotic pose estimation and autonomous driving [1,2]. Traditional stereo matching methods usually consist of four steps: initial matching cost calculation, matching cost aggregation, disparity prediction, and post-processing. These can be categorized into global and local algorithms [3]. Local strategies solely use constant measurement windows or changeable windows to calculate the preliminary cost. Global strategies normally treat an optimization task by minimizing a word goal characteristic that incorporates

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