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Research map / Learning theory and algorithms / Combinatorial and search optimization

Radial Distortion Triangulation

CVPR 2019 · Learning theory and algorithms › Combinatorial and search optimization · approach: Globally optimal geometric optimization · 8 citations (OpenAlex)

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Measured effects reported in the paper

What was changedWhereMeasuredEffect
Minimize reprojection error in distorted image space rather than undistorted image spaceTwo-view triangulation with radial distortion · Checkerboard, medium and wide FOVmean 3D error (mm): 0.5005 -> 0.4712; 1.4032 -> 1.1967Helped
Minimize reprojection error in distorted image space rather than undistorted image spaceTwo-view triangulation with radial distortion · Checkerboard, medium and wide FOVmean reprojection error (px): 0.1211 -> 0.1205; 0.1902 -> 0.1853Helped
Use distorted-space triangulation when distortion calibration is exactITD versus IT [25] · Checkerboard, 0% distortion errormean 3D error (mm): 0.0794 -> 0.0793; 0.1020 -> 0.1013No clear effect
Use distorted-space triangulation for points near image bordersITD versus IT [25] · Synthetic data, top 5% most distorted pointsmean 3D error (mm): 0.1339 -> 0.1319; 0.2314 -> 0.2162Helped
Use Newton root refinement from λ = 0 instead of fully solving the quartic (ablation)ITD · Radial distortion triangulationruntime (ns): 1190 -> 141Helped
Use Newton root refinement from λ = 0 instead of fully solving the quartic (ablation)ITD · Radial distortion triangulationmillion points/second: 0.84 -> 7.1Helped
Iteratively update the corrected distorted points while enforcing the epipolar constraintITD · Synthetic dataconvergence to global optimumHelped

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