BCA-MVSNet: Integrating BIFPN and CA for Enhanced Detail Texture in Multi-View Stereo Reconstruction
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| Publicado en: | Electronics vol. 14, no. 15 (2025), p. 2958-2984 |
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| Autor principal: | |
| Otros Autores: | , , |
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MDPI AG
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| Acceso en línea: | Citation/Abstract Full Text + Graphics Full Text - PDF |
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| 001 | 3239024077 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 2079-9292 | ||
| 024 | 7 | |a 10.3390/electronics14152958 |2 doi | |
| 035 | |a 3239024077 | ||
| 045 | 2 | |b d20250101 |b d20251231 | |
| 084 | |a 231458 |2 nlm | ||
| 100 | 1 | |a Long, Ning |u School of Network and Communication Engineering, Chengdu Technological University, Chengdu 610023, China; longning@cdtu.edu.cn | |
| 245 | 1 | |a BCA-MVSNet: Integrating BIFPN and CA for Enhanced Detail Texture in Multi-View Stereo Reconstruction | |
| 260 | |b MDPI AG |c 2025 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a The 3D point cloud generated by MVSNet has good scene integrity but lacks sensitivity to details, causing holes and non-dense areas in flat and weak-texture regions. To address this problem and enhance the point cloud information of weak-texture areas, the BCA-MVSNet network is proposed in this paper. The network integrates the Bidirectional Feature Pyramid Network (BIFPN) into the feature processing of the MVSNet backbone network to accurately extract the features of weak-texture regions. In the feature map fusion stage, the Coordinate Attention (CA) mechanism is introduced into 3DU-Net to obtain the position information on the channel dimension related to the direction, improve the detail feature extraction, optimize the depth map and improve the depth accuracy. The experimental results show that BCA-MVSNet not only improves the accuracy of detail texture reconstruction, but also effectively controls the computational overhead. In the DTU dataset, the Overall and Comp metrics of BCA-MVSNet are reduced by 10.2% and 2.6%, respectively; in the Tanksand Temples dataset, the Mean metrics of the eight scenarios are improved by 6.51%. Three scenes are shot by binocular camera, and the reconstruction quality is excellent in the weak-texture area by combining the camera parameters and the BCA-MVSNet model. | |
| 653 | |a Feature extraction | ||
| 653 | |a Accuracy | ||
| 653 | |a Datasets | ||
| 653 | |a Cameras | ||
| 653 | |a Deep learning | ||
| 653 | |a Image reconstruction | ||
| 653 | |a Computer vision | ||
| 653 | |a Computer terminals | ||
| 653 | |a Neural networks | ||
| 653 | |a Three dimensional models | ||
| 653 | |a Feature maps | ||
| 653 | |a Methods | ||
| 653 | |a Algorithms | ||
| 653 | |a Texture | ||
| 653 | |a Efficiency | ||
| 700 | 1 | |a Duan Zhengxu |u Research Institute of University of Electronic Science and Technology of China, Yibin 644000, China | |
| 700 | 1 | |a Hu, Xiao |u School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644005, China; chenmingju@suse.edu.cn | |
| 700 | 1 | |a Chen Mingju |u School of Automation and Information Engineering, Sichuan University of Science and Engineering, Yibin 644005, China; chenmingju@suse.edu.cn | |
| 773 | 0 | |t Electronics |g vol. 14, no. 15 (2025), p. 2958-2984 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3239024077/abstract/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text + Graphics |u https://www.proquest.com/docview/3239024077/fulltextwithgraphics/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3239024077/fulltextPDF/embedded/L8HZQI7Z43R0LA5T?source=fedsrch |