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Record W3017270536 · doi:10.1111/phor.12315

Geometric modelling and calibration of a spherical camera imaging system

2020· article· en· W3017270536 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueThe Photogrammetric Record · 2020
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsBundle adjustmentPinhole (optics)Computer visionArtificial intelligenceCamera resectioningCollinearityCalibrationComputer scienceOrientation (vector space)Pinhole camera modelDistortion (music)PixelLens (geology)Camera auto-calibrationOpticsMathematicsPhotogrammetryPhysicsGeometry

Abstract

fetched live from OpenAlex

Abstract The Ladybug5 is an integrated, multi‐camera system that features a near‐spherical field of view. It is commonly deployed on mobile mapping systems to collect imagery for 3D reality capture. This paper describes an approach for the geometric modelling and self‐calibration of this system. The collinearity equations of the pinhole camera model are augmented with five radial lens distortion terms to correct the severe barrel distortion. Weighted relative orientation stability constraints are added to the self‐calibrating bundle adjustment solution to enforce the angular and positional stability between the Ladybug5’s six cameras. Centimetre‐level 3D reconstruction accuracy can be achieved, with image‐space precision and object‐space accuracy improved by 92 % and 93 % , respectively, relative to a two‐term lens distortion model. Sub‐pixel interior orientation stability and millimetre‐level relative orientation stability were also demonstrated over a 10‐month period.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.994
Threshold uncertainty score0.352

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.005
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.028
GPT teacher head0.243
Teacher spread0.215 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it