我有一个简单的捆绑调整问题，有两个摄像头和 230 个点，我试图使用 Ceres 解决。我的目标是获得绝对最快的解决方案，但我看到的结果似乎与有关捆绑调整问题的文档相矛盾。
如此处所述：
one way to solve this problem is to set Solver::Options::linear_solver_type to SPARSE_NORMAL_CHOLESKY and call Solve(). And while this is a reasonable thing to do, bundle adjustment problems have a special sparsity structure that can be exploited to solve them much more efficiently. Ceres provides three specialized solvers (collectively known as Schur-based solvers) for this task.
但是，当我使用时DENSE_NORMAL_CHOLESKY，使用求解器设置：
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
options.linear_solver_type = ceres::DENSE_NORMAL_CHOLESKY;
options.minimizer_progress_to_stdout = false;
options.logging_type = ceres::SILENT;
options.max_num_iterations = 20;
它给了我：
Time (in seconds):
Preprocessor 0.006372
Residual only evaluation 0.000359 (12)
Jacobian & residual evaluation 0.003254 (12)
Linear solver 0.001549 (12)
Minimizer 0.008216
Postprocessor 0.000008
Total 0.014596
但是，当我切换到 SCHUR 求解器时，如下所示：
options.use_explicit_schur_complement = true;
options.sparse_linear_algebra_library_type = SUITE_SPARSE;
options.linear_solver_type = ceres::ITERATIVE_SCHUR;
options.minimizer_progress_to_stdout = false;
options.logging_type = ceres::SILENT;
options.max_num_iterations = 20;
options.preconditioner_type = SCHUR_JACOBI;
它运行速度较慢，具有：
Time (in seconds):
Preprocessor 0.007213
Residual only evaluation 0.000306 (10)
Jacobian & residual evaluation 0.002611 (10)
Linear solver 0.007781 (10)
Minimizer 0.013027
Postprocessor 0.000009
Total 0.020249
我能做些什么来获得更快的结果吗？我尝试过订购各种 linear_solver_type 和不同的预处理器。设置 options.num_threads = 8; 也没有明显的区别。我错过了什么吗？