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Record W2130316744 · doi:10.1109/cvprw.2008.4563132

Camera localization and building reconstruction from single monocular images

2008· article· en· W2130316744 on OpenAlexaff
Ruisheng Wang, Frank P. Ferrie

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsMcGill University
Fundersnot available
KeywordsComputer visionArtificial intelligenceComputer sciencePiecewiseImage planeNormalPlanarImage (mathematics)MathematicsComputer graphics (images)GeometrySurface (topology)Mathematical analysis

Abstract

fetched live from OpenAlex

This paper presents a new method for reconstructing rectilinear buildings from single images under the assumption of flat terrain. An intuition of the method is that, given an image composed of rectilinear buildings, the 3D buildings can be geometrically reconstructed by using the image only. The recovery algorithm is formulated in terms of two objective functions which are based on the equivalence between the vector normal to the interpretation plane in the image space and the vector normal to the rotated interpretation plane in the object space. These objective functions are minimized with respect to the camera pose, the building dimensions, locations and orientations to obtain estimates for the structure of the scene. The method potentially provides a solution for large-scale urban modelling using aerial images, and can be easily extended to deal with piecewise planar objects in a more general situation.

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.

How this classification was reachedexpand

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: Other design · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.938
Threshold uncertainty score0.221

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.016
GPT teacher head0.237
Teacher spread0.221 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreMethods

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations4
Published2008
Admission routes1
Has abstractyes

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