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pipeline-of-distributed-3d-reconstruction's Introduction

如下,分布式(大规模)三维重建的流程如下:

  1. Feature Extraction.
  2. Images Matching and View Graph Creation.
  3. View Graph Clustering.
  4. Cluster Reconstruction(These part can be implemented distributed).
  5. Fusion.

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可能会用到的第三方库

DBoW2

https://github.com/dorian3d/DBoW2

DBoW3

https://github.com/rmsalinas/DBow3

libvot

A C++11 multithread library for image retrieval http://hlzz.github.io/libvot

Normalized cut

https://github.com/iromu/Graclus

http://www.cs.utexas.edu/users/dml/Software/README_Graclus

GraphSfM

https://github.com/AIBluefisher/GraphSfM

论文, 知识

Tianwei Shen

https://home.cse.ust.hk/~tshenaa/#code

Video Google: A Text Retrieval Approach to Object Matching in Videos

http://www.robots.ox.ac.uk/~vgg/publications/papers/sivic03.pdf

Scalable Recognition with a Vocabulary Tree

http://www-inst.eecs.berkeley.edu/~cs294-6/fa06/papers/nister\_stewenius\_cvpr2006.pdf

Normalized Cuts

https://blog.csdn.net/Amy\_H/article/details/1907699

https://repository.upenn.edu/cgi/viewcontent.cgi?article=1101&context=cis\_papers

http://www.sci.utah.edu/~gerig/CS7960-S2010/handouts/Normalized%20Graph%20cuts.pdf

网络流--最大流最小割

https://www.cnblogs.com/fzl194/p/8855101.html

https://blog.csdn.net/zitian246/article/details/76218020

https://blog.csdn.net/juncoder/article/details/38894217

瑞利商与极值计算

https://seanwangjs.github.io/2017/11/27/rayleigh-quotient-maximum.html

拉普拉斯矩阵（Laplace Matrix）与瑞利熵（Rayleigh quotient）

https://www.cnblogs.com/xingshansi/p/6702188.html?utm\_source=itdadao&utm\_medium=referral

Graclus

https://github.com/iromu/Graclus

http://www.cs.utexas.edu/users/dml/Software/graclus.html

https://www.pcquest.com/graclus-a-fast-graph-clustering-tool/

Paper about Graclus

https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=4302760

深蓝学院--大规模三维重建

http://www.shenlanxueyuan.com/open/course/18/lesson/14/live/replay/223/entry?refererUrl=http%3A//www.shenlanxueyuan.com/open/course/explore

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