Hi, Steffen
Thanks for your paper and code. But I have a question about the way to find the cam with most reconstructed points.
It's easy to understand that the counter "nr_recon[c]" should bigger than the threshold "60", but why the middle norm "normsAll[c]" should bigger than 0.06 ?
In my view, according to the bear1 cross product R*bear2 formula( bear1 x (Rbear2)), we can get a normal perpendicular to vector "bear1" and "Rbear2", and the norm of the normal is positive correlation with the area between two vector. So I think the norm smaller, the rotation "R" will be more accurately, isn't it?
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// find cam with most reconstructed points |
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bool init = false; |
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for (int c = 0; c < nrCams; ++c) |
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{ |
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if (nr_recon[c] > 60 && |
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normsAll[c] > 0.06) |
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{ |
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init = true; |
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bestCam = c; |
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if (c > 0) |
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if (normsAll[c] > normsAll[c - 1]) |
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bestCam = c; |
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} |
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} |
Hi, Steffen
Thanks for your paper and code. But I have a question about the way to find the cam with most reconstructed points.
It's easy to understand that the counter "nr_recon[c]" should bigger than the threshold "60", but why the middle norm "normsAll[c]" should bigger than 0.06 ?
In my view, according to the bear1 cross product R*bear2 formula( bear1 x (Rbear2)), we can get a normal perpendicular to vector "bear1" and "Rbear2", and the norm of the normal is positive correlation with the area between two vector. So I think the norm smaller, the rotation "R" will be more accurately, isn't it?
MultiCol-SLAM/src/cMultiInitializer.cpp
Lines 178 to 191 in b68853c