Does bundle adjustment that solves for the focal length survive a wrong assumed focal length or lens distortion?
research/sensing-bafocal
With 12 views, wrong poses and a focal length told 5% wrong, a fixed-focal adjustment costs 1.2× the true-focal error (2.7× at 20%); solving for the focal returns the floor with the focal within 0.3%, and for k1 = −0.3 distortion 0.0034 falls to 0.0015, but not at 4 views or above 2 px noise. Synthetic only
What it found
Follows sensing-ba and sensing-baoutlier, which assumed known intrinsics. On the same synthetic rig (12 views, 0.5 px noise, every pose wrong) the solver is told a focal length off by −20% to +20%. With the focal held, the adjustment's mean surface error is 0.0015 at ±2%, 0.0018 at ±5% and 0.0039–0.0049 at ±20%, against 0.0014 for the true focal; adding one shared focal length to the unknowns gives 0.0014 from every start and recovers it to 0.3%. With k1 = −0.3 radial distortion unmodelled the error is 0.0034; solving for k1 gives 0.0015. The gain is 3.6× at 0.1 px of noise but about 1× from 1 px, and with 4 views none of the variants beat 0.0068 and the focal is 2.4% off. Limits: synthetic, the fitted distortion law is the rendering law, one shared focal, no outliers; the focal and k1 ranges are swept, not measured from a device; 6 seeds per cell.
A figure from its own output
Paper, code and results
More in Sensing, imaging and 3D
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- How big a sensor can a K-frame burst of a small sensor stand in for?Inverting the burst break-even formula: the largest sensor-area ratio a K-frame burst matches runs from √K in the dark to K in bright light, so 8 frames at 3 e⁻ read noise match a 4×-area frame from 4.5 e⁻ per pixel per frame, a 6× frame from 21 e⁻ and never an 8× frame; Monte Carlo agrees to 1% in variance at 18 settings. Model only, swept ranges not sourced to a device
- How many checkerboard photos does OpenCV camera calibration need before the focal length stops moving?On OpenCV's 13 real checkerboard photos, random subsets of 3 views put the focal length within 2% of the 13-view value in 99% of draws (5–95% band 2.3% wide), 12 views give 0.5%; noise-only re-renders predict the band, and a one-term lens model doubles held-out error. One camera, one board
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Produced with AI assistance (Claude). Results are from the code in this repository and are simulated or stylised unless the abstract says otherwise; nothing here implies affiliation with or endorsement by any lab, researcher or company cited. MIT licensed.