DCVSMNet: Double Cost Volume Stereo Matching Network

Fuente: arXiv
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Hauptverfasser: Tahmasebi, Mahmoud, Huq, Saif, Meehan, Kevin, McAfee, Marion
Format: Preprint
Veröffentlicht: 2024
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author Tahmasebi, Mahmoud
Huq, Saif
Meehan, Kevin
McAfee, Marion
author_facet Tahmasebi, Mahmoud
Huq, Saif
Meehan, Kevin
McAfee, Marion
contents We introduce Double Cost Volume Stereo Matching Network(DCVSMNet) which is a novel architecture characterised by by two small upper (group-wise) and lower (norm correlation) cost volumes. Each cost volume is processed separately, and a coupling module is proposed to fuse the geometry information extracted from the upper and lower cost volumes. DCVSMNet is a fast stereo matching network with a 67 ms inference time and strong generalization ability which can produce competitive results compared to state-of-the-art methods. The results on several bench mark datasets show that DCVSMNet achieves better accuracy than methods such as CGI-Stereo and BGNet at the cost of greater inference time.
format Preprint
id arxiv_https___arxiv_org_abs_2402_16473
institution arXiv
publishDate 2024
record_format arxiv
spellingShingle DCVSMNet: Double Cost Volume Stereo Matching Network
Tahmasebi, Mahmoud
Huq, Saif
Meehan, Kevin
McAfee, Marion
Computer Vision and Pattern Recognition
We introduce Double Cost Volume Stereo Matching Network(DCVSMNet) which is a novel architecture characterised by by two small upper (group-wise) and lower (norm correlation) cost volumes. Each cost volume is processed separately, and a coupling module is proposed to fuse the geometry information extracted from the upper and lower cost volumes. DCVSMNet is a fast stereo matching network with a 67 ms inference time and strong generalization ability which can produce competitive results compared to state-of-the-art methods. The results on several bench mark datasets show that DCVSMNet achieves better accuracy than methods such as CGI-Stereo and BGNet at the cost of greater inference time.
title DCVSMNet: Double Cost Volume Stereo Matching Network
topic Computer Vision and Pattern Recognition
url https://arxiv.org/abs/2402.16473