四川大学学报（自然科学版）2024，Vol.61Issue(4)：232-243,12.DOI:10.19907/j.0490-6756.2024.043007
基于引导优化的立体匹配网络
Guided refinement for stereo matching network
摘要
Abstract
Numerous challenges exist in achieving high-precision stereo matching for intricate areas,such as small structures and edge regions.To address the issue of fine stereo matching in detailed areas,we propose a stereo matching network based on guided refinement is proposed.Firstly,a guided refinement module is con-structed,utilizing guided deformable convolution.Unlike deformable convolution,this module performs off-set and modulation scalar learning on additional input guide features to enhance the deformation parameter learning ability of deformable convolution.Secondly,a guided refinement stereo matching network is de-signed based on the guided refinement module.This network introduces a three-level cascaded cost aggrega-tion module,incorporating 3D cost aggregation and 2D guided refinement aggregation,progressively refining the registration accuracy of detailed region.Experimental results demonstrate that,compared with state-of-the-art algorithms on standard datasets such as SceneFlow and KITTI,the proposed algorithm achieves high-precision registration of detailed regions.Notably,the applicability test results of the guided refinement mod-ule on the KITTI2015 datasets indicate that the D1-noc and D1-all values of advanced algorithms such as Gw-cNet and AANet increase by approximately 20%after integrating the guided refinement module.关键词
立体匹配/引导可变形卷积/引导聚合/多特征提取/边缘保持Key words
Stereo matching/Guided deformable convolution/Guide aggregation/Multi-feature extraction/Edge-preserving分类
信息技术与安全科学引用本文复制引用
李杰,昌明源,向泽林,都双丽,梁敏,李旭伟..基于引导优化的立体匹配网络[J].四川大学学报（自然科学版）,2024,61(4):232-243,12.基金项目
国家自然科学基金项目(61801279) （61801279）
山西省基础研究计划自然科学研究项目(202203021211333) （202203021211333）
山西省高等学校哲学社会科学研究项目(2021W058) （2021W058）
山西省基础研究计划青年科学研究项目(202103021223308) （202103021223308）
西安碑林区应用技术研发项目(GX2244) （GX2244）
