计算机与现代化Issue(5)：92-98,7.DOI:10.3969/j.issn.1006-2475.2024.05.016
结合局部自注意力和深度优化的多视图重建
Multi-view Reconstruction with Local Self-attention and Deep Optimization
摘要
Abstract
To address the issues of high memory and time consumption,low completeness and fidelity of high-resolution recon-struction in multi-view 3D reconstruction,we propose a deep learning-based multi-view reconstruction network.The network consists of a feature extraction module,a cascaded Patchmatch module and a depth map optimization module.First,we design a U-shaped feature extraction module to extract multi-stage feature maps,and introduce local self-attention layers with relative position encoding at each stage,which capture the local details and global context in the images,and enhance the feature extrac-tion performance of the network.Second,we design a deep residual network to fuse the features,and fully utilize the color image prior knowledge to constrain the depth map,and improve the accuracy of depth estimation.We test our network on the public da-taset DTU(Technical University of Denmark),and the experimental results show that our network achieves significant improve-ment in 3D reconstruction quality.Compared with PatchmatchNet,our network improves the completeness by 6.1%and the over-all by 2.5%.Compared with other SOTA(State-Of-The-Art)methods,our network also achieves better performance in both completeness and overall.关键词
深度学习/三维重建/局部自注意力/多视图立体/深度估计Key words
deep learning/3D reconstruction/local self-attention mechanism/multi-view stereo/depth estimation分类
信息技术与安全科学引用本文复制引用
叶森辉,王蕾..结合局部自注意力和深度优化的多视图重建[J].计算机与现代化,2024,(5):92-98,7.基金项目
国家自然科学基金资助项目(42001411) （42001411）
江西省核地学数据科学与系统工程技术研究中心基金资助项目(JELRGBDT202202) （JELRGBDT202202）
江西省放射性地学大数据技术工程实验室开放基金资助项目(JELRGBDT202103) （JELRGBDT202103）
