3D-Reconstruction 基于深度学习方法的单目多视图三维重建

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Deep Learning 3D-Reconstruction
Overview

基于深度学习方法的单目多视图三维重建

Part I 三维重建

代码：Part1

技术文档：[Markdown] [PDF]

原始图像：Original Images

点云结果：Point Cloud Results-1

效果图：

Part II 基于计算机视觉方法的点云到点云窗户识别

代码：Part2

技术文档：[Markdown] [PDF]

点云结果：Point Cloud Results-2

算法流程图：

Part III 基于ResNest的图像到点云的语义分割

代码：Part3

技术文档：[Markdown] [PDF]

语义分割结果：Semantic Segmentation Results

点云结果：Point Cloud Results-3

效果图：

参考文献

AA-RMVSNet [arXiv] [CVF] [PDF]

Wei Z, Zhu Q, Min C, et al. Aa-rmvsnet: Adaptive aggregation recurrent multi-view stereo network[C]//Proceedings of the IEEE/CVF International Conference on Computer Vision. 2021: 6187-6196.

Cascade-MVSNet [arXiv] [CVF] [PDF]

Gu X, Fan Z, Zhu S, et al. Cascade cost volume for high-resolution multi-view stereo and stereo matching[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2020: 2495-2504.

TransMVSNet [arXiv] [PDF]

Ding Y, Yuan W, Zhu Q, et al. TransMVSNet: Global Context-aware Multi-view Stereo Network with Transformers[J]. arXiv preprint arXiv:2111.14600, 2021.

LoFTR [arXiv] [CVF] [PDF]

Sun J, Shen Z, Wang Y, et al. LoFTR: Detector-free local feature matching with transformers[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 8922-8931.

PatchmatchNet [arXiv] [CVF] [PDF]

Wang F, Galliani S, Vogel C, et al. PatchmatchNet: Learned Multi-View Patchmatch Stereo[C]//Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2021: 14194-14203.

ResNeSt [arXiv] [PDF]

Zhang H, Wu C, Zhang Z, et al. Resnest: Split-attention networks[J]. arXiv preprint arXiv:2004.08955, 2020.

致谢

稀疏重建部分使用Colmap完成相机参数的获取。

稠密重建部分的代码主要来源于AA-RMVSNet。

点云切割与可视化使用CloudCompare及Meshlab完成。

调用Open3D进行表面重建。

Cascade+Transformer的代码主要基于kwea123实现的pytorch-lightning版本的Cascade-MVSNetl以及LoFTR进行实现。

窗户识别算法中部分思路参考了Color Space的矩形识别算法，图像处理技术主要基于冈萨雷斯的数字图像处理（第三版）。

语义分割部分调用了PyTorch-Encoding。

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