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Record W2156320849 · doi:10.1109/have.2008.4685303

Tele-immersive systems

2008· article· en· W2156320849 on OpenAlexaff
Chi Wa Leong, Yue Xing, Nicolas D. Georganas

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicAdvanced Vision and Imaging
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsVisual hullComputer scienceRendering (computer graphics)Computer visionArtificial intelligenceTexture mappingComputer graphics (images)Segmentation3D reconstructionHullIterative reconstructionGeology

Abstract

fetched live from OpenAlex

In this paper we will describe how we establish an experimental framework of tele-immersive system based on the polyhedral visual hull algorithm. The system is divided into two sites: the local and the remote sites. At the local site, images of a participant are taken from a set of synchronized cameras. A polyhedral visual hull algorithm is then used to reconstruct the 3D mesh. The texture information together with the mesh model is then sent to the remote site for rendering on immersive walls. We will describe each of the system components, including segmentation, post-process filtering, visual hull reconstruction at the local site; view dependent texture mapping at the remote site. We will present some preliminary results and observations we make during initial investigations, and propose possible future work that will add further functionalities to the system.

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: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.855
Threshold uncertainty score0.401

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 head0.023
GPT teacher head0.245
Teacher spread0.222 · 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
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

Citations5
Published2008
Admission routes1
Has abstractyes

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