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Added support for GV from known poses. #178
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I would rather not update the Aachen pipeline and keep |
Addressed the comments and reverted back the notebook. |
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def compute_epipolar_errors(qvec_r2t, tvec_r2t, p2d_r, p2d_t): |
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What's was the abbrevs of r
and t
?
r
for relative, t
for target? @mihaidusmanu
When GT poses are available, we can use them to compute the two-view geometries and filter the matches thus skipping the estimation of E/F/H.
This PR implements this functionality. The filtering is done based on distances to epipolar lines in normalized image coordinates (to avoid distortion). The parameter
max_error
ofgeometric_verification
is a multiplier of the detection noise. COLMAP uses by default 4px for SIFT (so if we assume a detection noise of 0.6-0.7px, we should have amax_error
of ~6).The current implementation takes 12 minutes to filter matches for the entire database model of Aachen (but it could potentially be multi-threaded if needed).
Below are some results with NetVLAD Top 20 + SuperPoint + SG on Aachen (obtain using the updated
pipeline_Aachen.ipynb
).Triangulation statistics
COLMAP GV (re-estimation)
GV from GT poses (max_error = 6.0)
GV from GT poses (max_error = 4.0)
Localization results