三维重建：SFM中BA的并行化

1. BA在重建中的作用

借鉴于运动中重建的方法，BA引入SLAM过程，而传统的滤波方法引入BA是跟随闭环检测出现。

1.1 BA在滤波方法中的嵌入

PTAM

1.2 BA在闭环检测之后的应用

在三维重建检测到闭环之后，则可以根据匹配结果，计算出总误差，并把误差平均到闭环之内的每一选定帧。

2. BA的并行化

借鉴于运动中重建，把所有的误差平均到每一个选定的关键帧里，对于帧数较多时，可以使用数据并行化。

数据并行化是方法并行化的必要条件，多帧数据进行平差的方法天然可适合并行处理。

并行BA：Multicore Bundle Adjustment

In this project, we consider the design and implementation of new inexact Newton type Bundle Adjustment algorithms that exploit hardware parallelism for efficiently solving large scale 3D scene reconstruction problems

即是 BA in the Large

Recent work in Structure from Motion has demonstrated the possibility ofreconstructing geometry from large-scale community photo collections. Bundle adjustment, the joint non-linear
refinement of camera and point parameters, is a key component of most SfM systems, and one which can consume a significant amount of time for large problems. As the number of photos in such collections continues to grow into the hundreds of thousands or even
millions, the scalability of bundle adjustment algorithms has become a critical issue.

In this project, we consider the design and implementation ofa new Inexact Newton type bundle adjustment algorithm, which usessubstantially less time and memory than standard Schur
complementbased methods, without compromising on the quality of the solution. Weexplore the use of the Conjugate Gradients algorithm for calculatingthe Newton step and its performance as a function of some simple andcomputationally efficient preconditioners.
We also show that the use of the Schur complementis not limited to factorization-based methods, how it can be used as part of the Conjugate Gradients (CG) method without incurring the computational cost of actually calculating and storing it in memory, and
how this use is equivalent to the choice of a particular preconditioner.

Paper

Bundle Adjustment in the Large

    Sameer Agarwal, Noah Snavely, Steven M. Seitz and Richard Szeliski