Hold-Out Self-Validation Cannot Certify the Accuracy of Photogrammetric Reconstructions
Hold-Out Self-Validation Cannot Certify the Accuracy of Photogrammetric Reconstructions
This research paper evaluates hold-out self-validation as a substitute for independent accuracy checks in photogrammetric reconstructions used for inspection.
The study demonstrates that hold-out self-validation methods fail to certify accuracy in photogrammetric reconstructions, showing score saturation and inability to detect systematic errors across multiple datasets including GNSS-referenced captures and synthetic benchmarks.
Source: arXiv Robotics — research abstracts · Read original article ↗
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What the source reports
Publisher-reported claims, with original evidence. These results have not been independently verified by RoboSignal.
Reported numbers
reconstructions tested
40
View original evidence
on ETH3D and EuRoC, and on 30 IMC 2025 scenes. The standard form re-localises each withheld image only against points seen by at least two retained images. Its score saturates. It stayed at 1.00 in all 40 thinned reconstructions while camera-position error varied by a factor of up to 5.9 within one capture. It also scored 1.00 on a corrupted model with only one of 226 cameras within 5 m. Reprojection error ranked som
Open source S3corrupted runs evaluated
18
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e of the worst models best. A few lost cameras can raise the least-squares RMSE to the size of the block, so we also evaluate with a robust alignment. Taking the correspondences from the verified matches lowered the score below 0.95 in 14 of 18 corrupted runs, against 4 for the standard form and 10 when its failed resections count as errors, with all seven nominal runs at 1.00. Mapping the block again without the wit
Open source S4nominal runs
7
View original evidence
e of the worst models best. A few lost cameras can raise the least-squares RMSE to the size of the block, so we also evaluate with a robust alignment. Taking the correspondences from the verified matches lowered the score below 0.95 in 14 of 18 corrupted runs, against 4 for the standard form and 10 when its failed resections count as errors, with all seven nominal runs at 1.00. Mapping the block again without the wit
Open source S4IMC 2025 scenes
30
View original evidence
on ETH3D and EuRoC, and on 30 IMC 2025 scenes. The standard form re-localises each withheld image only against points seen by at least two retained images. Its score saturates. It stayed at 1.00 in all 40 thinned reconstructions while camera-position error varied by a factor of up to 5.9 within one capture. It also scored 1.00 on a corrupted model with only one of 226 cameras within 5 m. Reprojection error ranked som
Open source S3correspondences from verified matches
14
View original evidence
e of the worst models best. A few lost cameras can raise the least-squares RMSE to the size of the block, so we also evaluate with a robust alignment. Taking the correspondences from the verified matches lowered the score below 0.95 in 14 of 18 corrupted runs, against 4 for the standard form and 10 when its failed resections count as errors, with all seven nominal runs at 1.00. Mapping the block again without the wit
Open source S4failed resections count as errors
10
View original evidence
e of the worst models best. A few lost cameras can raise the least-squares RMSE to the size of the block, so we also evaluate with a robust alignment. Taking the correspondences from the verified matches lowered the score below 0.95 in 14 of 18 corrupted runs, against 4 for the standard form and 10 when its failed resections count as errors, with all seven nominal runs at 1.00. Mapping the block again without the wit
Open source S4
- paper: not_reported
Source excerpts and review record
Automatically extracted; no manual editorial approval recorded.
on ETH3D and EuRoC, and on 30 IMC 2025 scenes. The standard form re-localises each withheld image only against points seen by at least two retained images. Its score saturates. It stayed at 1.00 in all 40 thinned reconstructions while camera-position error varied by a factor of up to 5.9 within one capture. It also scored 1.00 on a corrupted model with only one of 226 cameras within 5 m. Reprojection error ranked som
Open source S3
e of the worst models best. A few lost cameras can raise the least-squares RMSE to the size of the block, so we also evaluate with a robust alignment. Taking the correspondences from the verified matches lowered the score below 0.95 in 14 of 18 corrupted runs, against 4 for the standard form and 10 when its failed resections count as errors, with all seven nominal runs at 1.00. Mapping the block again without the wit
Open source S4
arXiv:2607.24852v5 Announce Type: replace-cross Abstract: Photogrammetric reconstructions used for inspection are often delivered without an independent accuracy check. Hold-out self-validation is a possible substitute. Images are withheld, their orientations are re-estimated from the rest of the block, and agreement is taken as evidence of quality. We test it on ten GNSS-referenced aerial captures from eight sites,
Open source S2
Source：arXiv Robotics — research abstracts · arxiv.org