FE-RMVSNet: a feature-enhanced 3D reconstruction network

Jin Liu; Jialong Li; Kailin Chen; Jingjing Chen

doi:10.1117/12.3037204

22 October 2024 FE-RMVSNet: a feature-enhanced 3D reconstruction network

Jin Liu, Jialong Li, Kailin Chen, Jingjing Chen

Proceedings Volume 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024); 132740U (2024) https://doi.org/10.1117/12.3037204
Event: Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 2024, Haikou, HI, China

Abstract

This study introduces FE-RMVSNet, a novel deep learning-based multi-view 3D reconstruction network. It effectively addresses common challenges in 3D reconstruction, such as occlusion and computational limitations, by enhancing feature extraction. FE-RMVSNet demonstrates superior performance in both accuracy and completeness compared to existing methods.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jin Liu, Jialong Li, Kailin Chen, and Jingjing Chen "FE-RMVSNet: a feature-enhanced 3D reconstruction network", Proc. SPIE 13274, Sixteenth International Conference on Digital Image Processing (ICDIP 2024), 132740U (22 October 2024); https://doi.org/10.1117/12.3037204

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KEYWORDS

3D modeling

Performance modeling

Education and training

Feature extraction

Convolution

Point clouds

Visual process modeling

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