Computing Covariance - How to improve performance

Alan Buchanan

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Jul 11, 2017, 8:47:44 AM7/11/17

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Hi Sameer,

I am computing the covariance for the interior and exterior orientation from a reasonably large dataset (750 high resolution 8100x4530 pixels).

The performance is very slow whereas the rest of the adjustment is reasonably fast. It is still crunching numbers and has been going for over an hour.

I am using openMVG with the default Ceres install on a windows machine (12GB ram and a decent graphics card).

I am using the following options.

ceres::Covariance::Options options;

options.num_threads = 7; (I changed to 7 from the default of 1)

options.algorithm_type = ceres::EIGEN_SPARSE_QR;

ceres::Covariance covariance(options);

The rest is as per the example in the documentation

Are there any tricks to speeding this up or is it merely the fact we are trying to invert a very large matrix and have t live with it.

Regards,

Alan

Sameer Agarwal

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Jul 11, 2017, 9:29:55 AM7/11/17

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options.algorithm_type = SUITE_SPARSE_QR (You will need SuiteSparseQR installed for this to work) is your best bet to do this right now.

Sameer

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Alan Buchanan

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Jul 11, 2017, 10:27:24 AM7/11/17

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Thanks Same error I will have look at downloading and installing this

On 11 Jul 2017 21:29, "'Sameer Agarwal' via Ceres Solver" <ceres-...@googlegroups.com> wrote:

options.algorithm_type = SUITE_SPARSE_QR (You will need SuiteSparseQR installed for this to work) is your best bet to do this right now.
Sameer

On Tue, Jul 11, 2017 at 5:47 AM Alan Buchanan <abuc...@gmail.com> wrote:

Hi Sameer,

I am computing the covariance for the interior and exterior orientation from a reasonably large dataset (750 high resolution 8100x4530 pixels).

The performance is very slow whereas the rest of the adjustment is reasonably fast. It is still crunching numbers and has been going for over an hour.

I am using openMVG with the default Ceres install on a windows machine (12GB ram and a decent graphics card).

I am using the following options.

ceres::Covariance::Options options;
options.num_threads = 7; (I changed to 7 from the default of 1)
options.algorithm_type = ceres::EIGEN_SPARSE_QR;
ceres::Covariance covariance(options);

The rest is as per the example in the documentation

Are there any tricks to speeding this up or is it merely the fact we are trying to invert a very large matrix and have t live with it.

Regards,

Alan

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Alan Buchanan

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Jul 12, 2017, 6:53:24 AM7/12/17

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Just hold off on my last question.

I noticed that although I enable suite sparse in Cmake it did not generate a project with it. Apparantly I have to also include LAPACK.

I will let you know later if I still have problems

Alan Buchanan

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Jul 12, 2017, 8:19:56 AM7/12/17

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Sameer,

I have spent a lot of time trying install ceres with suit sparse on windows. Looking on google it appears that other people are having issues with installing suit sparse and Lapack.

I have ubuntu linux box at home that I think I will bite the bullet and install on there as it looks much easier.