MétaCan
Menu
Back to cohort
Record W4410049275 · doi:10.1145/3722570.3726877

A Semi-automated Mesh Editing Pipeline for HRTF Simulation

2025· article· en· W4410049275 on OpenAlexaff
Bodee Quansah, Navid H. Zandi, Rong Zheng

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
Topic3D Shape Modeling and Analysis
Canadian institutionsMcMaster University
Fundersnot available
KeywordsPipeline (software)Computer scienceComputer graphics (images)Speech recognitionOperating system

Abstract

fetched live from OpenAlex

Head-related transfer functions (HRTFs), which are useful for rendering realistic binaural audio, depend on an individual's unique ear and head morphology. Although direct measurement of HRTFs is accurate, it is also resource-intensive, making simulation an attractive alternative. However, 3D scanner outputs often require extensive editing to meet the quality requirements for simulation. This work presents a semi-automated pipeline that streamlines mesh preparation by non-rigidly registering a template to 3D scans to produce meshes suitable for acoustic simulation. The most accurate pipeline achieved a worst-case reconstruction error of 0.8 mm globally and landmark errors of 3 mm and 2.6 mm for the left and right ears, respectively.

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: Simulation or modeling · Consensus signal: Simulation or modeling
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.986
Threshold uncertainty score0.280

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)0.0000.000

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 head0.011
GPT teacher head0.272
Teacher spread0.261 · 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 designSimulation or modeling
Domainnot available
GenreEmpirical

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

Citations0
Published2025
Admission routes1
Has abstractyes

Explore more

Same topic3D Shape Modeling and AnalysisFrench-language works237,207