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Record W2086145568 · doi:10.1145/2560328

Flow-complex-based shape reconstruction from 3D curves

2014· article· en· W2086145568 on OpenAlex
Bardia Sadri, Karan Singh

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.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueACM Transactions on Graphics · 2014
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsUniversity of Toronto
FundersMitacs
KeywordsEmbeddingIntersection (aeronautics)Computer scienceParametric equationTopology (electrical circuits)Range (aeronautics)AlgorithmGeometryMathematicsParametric statisticsArtificial intelligenceCombinatorics

Abstract

fetched live from OpenAlex

We address the problem of shape reconstruction from a sparse unorganized collection of 3D curves, typically generated by increasingly popular 3D curve sketching applications. Experimentally, we observe that human understanding of shape from connected 3D curves is largely consistent, and informed by both topological connectivity and geometry of the curves. We thus employ the flow complex , a structure that captures aspects of input topology and geometry, in a novel algorithm to produce an intersection-free 3D triangulated shape that interpolates the input 3D curves. Our approach is able to triangulate highly nonplanar and concave curve cycles, providing a robust 3D mesh and parametric embedding for challenging 3D curve input. Our evaluation is fourfold: we show our algorithm to match designer-selected curve cycles for surfacing; we produce user-acceptable shapes for a wide range of curve inputs; we show our approach to be predictable and robust to curve addition and deletion; we compare our results to prior art.

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: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.932
Threshold uncertainty score0.747

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 communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.022
GPT teacher head0.217
Teacher spread0.195 · 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