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Record W2604315660 · doi:10.1109/vr.2017.7892302

Estimating the motion-to-photon latency in head mounted displays

2017· article· en· W2604315660 on OpenAlex
Jingbo Zhao, Robert S. Allison, Margarita Vinnikov, Sion Jennings

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
FieldNeuroscience
TopicVisual perception and processing mechanisms
Canadian institutionsNational Research Council CanadaYork University
Fundersnot available
KeywordsOptical head-mounted displayComputer scienceLatency (audio)SwingComputer visionVirtual realityArtificial intelligenceComputer graphics (images)PhysicsAcousticsTelecommunications

Abstract

fetched live from OpenAlex

We present a method for estimating the Motion-to-Photon (End-to-End) latency of head mounted displays (HMDs). The specific HMD evaluated in our study was the Oculus Rift DK2, but the procedure is general. We mounted the HMD on a pendulum to introduce damped sinusoidal motion to the HMD during the pendulum swing. The latency was estimated by calculating the phase shift between the captured signals of the physical motion of the HMD and a motion-dependent gradient stimulus rendered on the display. We used the proposed method to estimate both rotational and translational Motion-to-Photon latencies of the Oculus Rift DK2.

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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.786
Threshold uncertainty score0.671

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.001

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 head0.088
GPT teacher head0.398
Teacher spread0.310 · 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