A stereo matching algorithm based on the improved PSMNet.

Deep learning based on a convolutional neural network (CNN) has been successfully applied to stereo matching. Compared with the traditional method, the speed and accuracy of this method have been greatly improved. However, the existing stereo matching framework based on a CNN often encounters two pr...

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Autores principales:	Zedong Huang, Jinan Gu, Jing Li, Xuefei Yu
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/318bf79f1e0f4d60a0d3ba5b0d223b02
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spelling	oai:doaj.org-article:318bf79f1e0f4d60a0d3ba5b0d223b022021-12-02T20:17:48ZA stereo matching algorithm based on the improved PSMNet.1932-620310.1371/journal.pone.0251657https://doaj.org/article/318bf79f1e0f4d60a0d3ba5b0d223b022021-01-01T00:00:00Zhttps://doi.org/10.1371/journal.pone.0251657https://doaj.org/toc/1932-6203Deep learning based on a convolutional neural network (CNN) has been successfully applied to stereo matching. Compared with the traditional method, the speed and accuracy of this method have been greatly improved. However, the existing stereo matching framework based on a CNN often encounters two problems. First, the existing stereo matching network has many parameters, which leads to the matching running time being too long. Second, the disparity estimation is inadequate in some regions where reflections, repeated textures, and fine structures may lead to ill-posed problems. Through the lightweight improvement of the PSMNet (Pyramid Stereo Matching Network) model, the common matching effect of ill-conditioned areas such as repeated texture areas and weak texture areas is solved. In the feature extraction part, ResNeXt is introduced to learn unitary feature extraction, and the ASPP (Atrous Spatial Pyramid Pooling) module is trained to extract multiscale spatial feature information. The feature fusion module is designed to effectively fuse the feature information of different scales to construct the matching cost volume. The improved 3D CNN uses the stacked encoding and decoding structure to further regularize the matching cost volume and obtain the corresponding relationship between feature points under different parallax conditions. Finally, the disparity map is obtained by a regression. We evaluate our method on the Scene Flow, KITTI 2012, and KITTI 2015 stereo datasets. The experiments show that the proposed stereo matching network achieves a comparable prediction accuracy and much faster running speed compared with PSMNet.Zedong HuangJinan GuJing LiXuefei YuPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 16, Iss 8, p e0251657 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Zedong Huang Jinan Gu Jing Li Xuefei Yu A stereo matching algorithm based on the improved PSMNet.
description	Deep learning based on a convolutional neural network (CNN) has been successfully applied to stereo matching. Compared with the traditional method, the speed and accuracy of this method have been greatly improved. However, the existing stereo matching framework based on a CNN often encounters two problems. First, the existing stereo matching network has many parameters, which leads to the matching running time being too long. Second, the disparity estimation is inadequate in some regions where reflections, repeated textures, and fine structures may lead to ill-posed problems. Through the lightweight improvement of the PSMNet (Pyramid Stereo Matching Network) model, the common matching effect of ill-conditioned areas such as repeated texture areas and weak texture areas is solved. In the feature extraction part, ResNeXt is introduced to learn unitary feature extraction, and the ASPP (Atrous Spatial Pyramid Pooling) module is trained to extract multiscale spatial feature information. The feature fusion module is designed to effectively fuse the feature information of different scales to construct the matching cost volume. The improved 3D CNN uses the stacked encoding and decoding structure to further regularize the matching cost volume and obtain the corresponding relationship between feature points under different parallax conditions. Finally, the disparity map is obtained by a regression. We evaluate our method on the Scene Flow, KITTI 2012, and KITTI 2015 stereo datasets. The experiments show that the proposed stereo matching network achieves a comparable prediction accuracy and much faster running speed compared with PSMNet.
format	article
author	Zedong Huang Jinan Gu Jing Li Xuefei Yu
author_facet	Zedong Huang Jinan Gu Jing Li Xuefei Yu
author_sort	Zedong Huang
title	A stereo matching algorithm based on the improved PSMNet.
title_short	A stereo matching algorithm based on the improved PSMNet.
title_full	A stereo matching algorithm based on the improved PSMNet.
title_fullStr	A stereo matching algorithm based on the improved PSMNet.
title_full_unstemmed	A stereo matching algorithm based on the improved PSMNet.
title_sort	stereo matching algorithm based on the improved psmnet.
publisher	Public Library of Science (PLoS)
publishDate	2021
url	https://doaj.org/article/318bf79f1e0f4d60a0d3ba5b0d223b02
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A stereo matching algorithm based on the improved PSMNet.

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