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Research on Multi-View Stereo Network Based on Self-Attention Mechanism Cover

Research on Multi-View Stereo Network Based on Self-Attention Mechanism

By: Wenkai Li,  Jun Yu,  Leilei Fan and  Zhiyi Hu  
Open Access
|Sep 2025

Figures & Tables

Figure 1.

Cost Volume Regularization Network Architecture
Cost Volume Regularization Network Architecture

Figure 2.

Self-attention Architecture
Self-attention Architecture

Figure 3.

The Enhanced Feature Extraction Block
The Enhanced Feature Extraction Block

Figure 4.

Residual Block
Residual Block

Figure 5.

Pre-activated Residual Block
Pre-activated Residual Block

Figure 6.

Improved Cost Volume Regularization Network
Improved Cost Volume Regularization Network

Figure 7.

Partial Scenes in DTU Dataset
Partial Scenes in DTU Dataset

Figure 8.

Loss Curve
Loss Curve

Figure 9.

Depth Map Results
Depth Map Results

Figure 10.

Comparison of the generated point cloud models
Comparison of the generated point cloud models

Experimental Parameters

parametervalue
batch size1
learning rate0.001
epoch16
Adam-β10.9
Adamβ20.999

Comparison of Different 3D Reconstruction

Methods(Acc)/mm(Comp)/mm(OA)/mm
Colmap0.4000.6640.532
Gipuma0.2830.8730.578
Camp0.8350.5540.695
MVSNet0.4560.5740.515
PointMVSNet0.4350.4750.455
SelfResMVSNet0.4420.5620.502
Language: English
Page range: 1 - 10
Published on: Sep 30, 2025
Published by: Xi’an Technological University
In partnership with: Paradigm Publishing Services
Publication frequency: 4 issues per year

© 2025 Wenkai Li, Jun Yu, Leilei Fan, Zhiyi Hu, published by Xi’an Technological University
This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 License.