Context-Aware Multi-view Stereo Network for Efficient Edge-Preserving Depth Estimation.
Saved in:
| Title: | Context-Aware Multi-view Stereo Network for Efficient Edge-Preserving Depth Estimation. |
|---|---|
| Authors: | Su, Wanjuan1 (AUTHOR) suwanjuan@hust.edu.cn, Tao, Wenbing1 (AUTHOR) wenbingtao@hust.edu.cn |
| Source: | International Journal of Computer Vision. Jun2025, Vol. 133 Issue 6, p3367-3391. 25p. |
| Subjects: | Convolutional neural networks, Stereo image processing, Artificial intelligence, Learning modules, Temples |
| Abstract: | Learning-based multi-view stereo methods have achieved great progress in recent years by employing the coarse-to-fine depth estimation framework. However, existing methods still encounter difficulties in recovering depth in featureless areas, object boundaries, and thin structures which mainly due to the poor distinguishability of matching clues in low-textured regions, the inherently smooth properties of 3D convolution neural networks used for cost volume regularization, and information loss of the coarsest scale features. To address these issues, we propose a Context-Aware multi-view stereo Network (CANet) that leverages contextual cues in images to achieve efficient edge-preserving depth estimation. The structural self-similarity information in the reference view is exploited by the introduced self-similarity attended cost aggregation module to perform long-range dependencies modeling in the cost volume, which can boost the matchability of featureless regions. The context information in the reference view is subsequently utilized to progressively refine multi-scale depth estimation through the proposed hierarchical edge-preserving residual learning module, resulting in delicate depth estimation at edges. To enrich features at the coarsest scale by making it focus more on delicate areas, a focal selection module is presented which can enhance the recovery of initial depth with finer details such as thin structure. By integrating the strategies above into the well-designed lightweight cascade framework, CANet achieves superior performance and efficiency trade-offs. Extensive experiments show that the proposed method achieves state-of-the-art performance with fast inference speed and low memory usage. Notably, CANet ranks first on challenging Tanks and Temples advanced dataset and ETH3D high-res benchmark among all published learning-based methods. [ABSTRACT FROM AUTHOR] |
| Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 185069259 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: Context-Aware Multi-view Stereo Network for Efficient Edge-Preserving Depth Estimation. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Su%2C+Wanjuan%22">Su, Wanjuan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> suwanjuan@hust.edu.cn</i><br /><searchLink fieldCode="AR" term="%22Tao%2C+Wenbing%22">Tao, Wenbing</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> wenbingtao@hust.edu.cn</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22International+Journal+of+Computer+Vision%22">International Journal of Computer Vision</searchLink>. Jun2025, Vol. 133 Issue 6, p3367-3391. 25p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br /><searchLink fieldCode="DE" term="%22Stereo+image+processing%22">Stereo image processing</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink><br /><searchLink fieldCode="DE" term="%22Learning+modules%22">Learning modules</searchLink><br /><searchLink fieldCode="DE" term="%22Temples%22">Temples</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Learning-based multi-view stereo methods have achieved great progress in recent years by employing the coarse-to-fine depth estimation framework. However, existing methods still encounter difficulties in recovering depth in featureless areas, object boundaries, and thin structures which mainly due to the poor distinguishability of matching clues in low-textured regions, the inherently smooth properties of 3D convolution neural networks used for cost volume regularization, and information loss of the coarsest scale features. To address these issues, we propose a Context-Aware multi-view stereo Network (CANet) that leverages contextual cues in images to achieve efficient edge-preserving depth estimation. The structural self-similarity information in the reference view is exploited by the introduced self-similarity attended cost aggregation module to perform long-range dependencies modeling in the cost volume, which can boost the matchability of featureless regions. The context information in the reference view is subsequently utilized to progressively refine multi-scale depth estimation through the proposed hierarchical edge-preserving residual learning module, resulting in delicate depth estimation at edges. To enrich features at the coarsest scale by making it focus more on delicate areas, a focal selection module is presented which can enhance the recovery of initial depth with finer details such as thin structure. By integrating the strategies above into the well-designed lightweight cascade framework, CANet achieves superior performance and efficiency trade-offs. Extensive experiments show that the proposed method achieves state-of-the-art performance with fast inference speed and low memory usage. Notably, CANet ranks first on challenging Tanks and Temples advanced dataset and ETH3D high-res benchmark among all published learning-based methods. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of International Journal of Computer Vision is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=185069259 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11263-024-02337-8 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 25 StartPage: 3367 Subjects: – SubjectFull: Convolutional neural networks Type: general – SubjectFull: Stereo image processing Type: general – SubjectFull: Artificial intelligence Type: general – SubjectFull: Learning modules Type: general – SubjectFull: Temples Type: general Titles: – TitleFull: Context-Aware Multi-view Stereo Network for Efficient Edge-Preserving Depth Estimation. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Su, Wanjuan – PersonEntity: Name: NameFull: Tao, Wenbing IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 06 Text: Jun2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09205691 Numbering: – Type: volume Value: 133 – Type: issue Value: 6 Titles: – TitleFull: International Journal of Computer Vision Type: main |
| ResultId | 1 |
