MétaCan
Menu
Back to cohort
Record W2115706284 · doi:10.1109/ccece.2006.277767

Lazy Generation of Building Interiors in Realtime

2006· article· en· W2115706284 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

Venuenot available
Typearticle
Languageen
FieldComputer Science
TopicComputer Graphics and Visualization Techniques
Canadian institutionsCarleton University
Fundersnot available
KeywordsComputer scienceDoorsScheme (mathematics)Frame (networking)MetaverseVirtual worldVirtual realityHuman–computer interactionOperating systemTelecommunications

Abstract

fetched live from OpenAlex

Impenetrable doors are often quite common in virtual worlds. This is especially apparent in video games boasting large urban environments. Although there are often enterable buildings in these games, the vast majority of the buildings cannot be entered. Given limited development times and memory limitations, it is infeasible for developers to create such a large number of building interiors for the player to explore. Automatic real-time building interior generation can provide a means of entry through every visible door in a virtual world. We present a novel approach to generate virtual building interiors in real-time. Although a building in its entirety may be quite large, only the portions of the building that are needed immediately are generated. This lazy generation scheme allows the use of only a fraction of the memory that a model of the entire building requires. Our method provides realtime frame rates, making it attractive for realtime interactive applications

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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.964
Threshold uncertainty score0.168

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 head0.024
GPT teacher head0.295
Teacher spread0.271 · 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