西安工程大学学报2024，Vol.38Issue(1)：121-130,10.DOI:10.13338/j.issn.1674-649x.2024.01.016
一种密集多尺度特征引导代价聚合的改进立体匹配网络
Improved stereo matching network based on dense multi-scale feature guided cost aggregation
摘要
Abstract
To further improve the disparity prediction accuracy of stereo matching algorithm in the ill-posed regions such as repeating textures,no texture,and edge,an improved dense multi-scale feature guided aggregation network(DGNet)based on PSMNet was proposed.Firstly,a dense multi-scale feature extraction module was designed based on the dense atrous spatial pyra-mid pooling structure.This module extracted region-level features of different scales by using at-rous convolution of different expansion rates,and effectively fused image features of different scales through dense connection,so that the network can capture contextual information.Sec-ondly,the initial cost volume was obtained by concatenating left feature maps with their corre-sponding right feature maps across each disparity level.Then,a dense multi-scale feature guided cost aggregation module was proposed,which adaptively fused the cost volume and dense multi-scale features while aggregating the cost volume,so that the subsequent decoding layers can de-code more accurate and high-resolution geometry information with the guidance of multi-scale context information.Finally,the high-resolution cost volume with global optimization was input into the regression module to obtain the disparity map.Comprehensive experimental results dem-onstrated that the mismatching rate of the proposed algorithm on KITTI 2015 and KITTI 2012 datasets was respectively reduced to 1.76%and 1.24%,and the endpoint error on SceneFlow dataset was reduced to 0.56 px.Compared with existing stereo matching algorithms such as GWCNet and CPOP-Net,the proposed algorithm performs well in the ill-posed regions.关键词
双目视觉/立体匹配/密度多尺度特征/自适应融合Key words
binocular vision/stereo matching/dense multi-scale features/adaptive fusion分类
信息技术与安全科学引用本文复制引用
张博,张美灵,李雪,朱磊..一种密集多尺度特征引导代价聚合的改进立体匹配网络[J].西安工程大学学报,2024,38(1):121-130,10.基金项目
国家自然科学基金(61971339) （61971339）
陕西省自然科学基础研究计划(2019JQ-361) （2019JQ-361）
陕西省教育厅科研计划项目自然科学专项(19JK0361) （19JK0361）
