P3SNet: Parallel Pyramid Pooling Stereo Network

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dc.authorid0000-0001-8161-7181
dc.contributor.authorEmlek, Alper
dc.contributor.authorPeker, Murat
dc.date.accessioned2026-02-12T21:04:57Z
dc.date.available2026-02-12T21:04:57Z
dc.date.issued2023
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractIn autonomous driving and advanced driver assistance systems (ADAS), stereo matching is a challenging research topic. Recent work has shown that high-accuracy disparity maps can be obtained with end-to-end training with the help of deep convolutional neural networks from stereo images. However, many of these methods suffer from long run-time for real-time studies. Therefore, in this paper, we introduce P3SNet, which can generate both real-time results and competitive disparity maps to the state-of-the-art. P3SNet architecture consists of two main modules: parallel pyramid pooling and hierarchical disparity aggregation. The parallel pyramid pooling structure makes it possible to obtain local and global information intensively from its multi-scale features. The hierarchical disparity aggregation provides multi-scale disparity maps by using a coarse-to-fine training strategy with the help of the costs obtained from multi-scale features. The proposed approach was evaluated on several benchmark datasets. The results on all datasets showed that the proposed P3SNet achieved better or competitive results while having lower runtime. The code is available at https://github.com/aemlek/P3SNet.
dc.description.sponsorshipNigde Omer Halisdemir University Research Project Unit [MMT 2019/7-BAGEP]
dc.description.sponsorshipThis work was supported by the Nigde Omer Halisdemir University Research Project Unit under Grant MMT 2019/7-BAGEP.
dc.identifier.doi10.1109/TITS.2023.3276328
dc.identifier.endpage10444
dc.identifier.issn1524-9050
dc.identifier.issn1558-0016
dc.identifier.issue10
dc.identifier.scopus2-s2.0-85161039336
dc.identifier.scopusqualityQ1
dc.identifier.startpage10433
dc.identifier.urihttps://doi.org/10.1109/TITS.2023.3276328
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6741
dc.identifier.volume24
dc.identifier.wosWOS:001005674600001
dc.identifier.wosqualityQ1
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherIeee-Inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions on Intelligent Transportation Systems
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/closedAccess
dc.snmzKA_WoS_20260212
dc.subjectStereo matching
dc.subjectdisparity estimation
dc.subjectconvolutional neural network
dc.titleP3SNet: Parallel Pyramid Pooling Stereo Network
dc.typeArticle

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