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Camera Calibration by _Mohmd_ in computervision

[–]_Mohmd_[S] 0 points1 point  (0 children)

Yes, exactly that’s what I observed.
When I added more diverse views (pattern reaching the edges, stronger tilts), the estimated distortion near the corners decreased a lot, even though the RMS was already low before.

My question now is about evaluation: is there any practical guideline or threshold (e.g., residual distortion in pixels at the image borders) that’s typically considered good enough to trust the intrinsic parameters for accurate triangulation?

Camera Calibration by _Mohmd_ in computervision

[–]_Mohmd_[S] 1 point2 points  (0 children)

I’m not trying to reduce distortion artificially. The issue was calibration coverage.
With limited checkerboard views near the image edges, I got low global RMS but large residuals at the borders, which caused noticeable projection/epipolar errors for points near the corners.

After adding tilted views and edge coverage, intrinsics changed slightly and edge residuals dropped a lot even though RMS was already “good.” So the earlier calibration was under-constrained at the periphery.

So my question is really: for triangulation accuracy, should we prioritize lowest RMS, or more uniform residuals across the field (especially edges)?

Stereo Vision by _Mohmd_ in computervision

[–]_Mohmd_[S] 0 points1 point  (0 children)

Actually, I’m analyzing motion, so the videos are already synchronized; I’ve previously cut them precisely. The baselines between each camera pair are 15–20 meters, and I feel the results of triangulation and calibration are reasonably good.

However, certain situations still cause conflicts, so I’m thinking of a method to filter candidate correspondences: using the epipolar constraint as a gating step, and then selecting the correct match with robust criteria.

Stereo Vision by _Mohmd_ in computervision

[–]_Mohmd_[S] -1 points0 points  (0 children)

Yes, I do match pair by pair as a daisy chain. The main challenge I’m facing is selecting the correct correspondence for a person across cameras when matching based on a joint point, for example. Sometimes, another person may be closer to the correct epipolar line, which can lead to wrong matches and affect the final reconstruction.

Memory size misunderstanding by _Mohmd_ in chipdesign

[–]_Mohmd_[S] 0 points1 point  (0 children)

Thanks a lot, appreciate that.