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Record W2576140020 · doi:10.1109/ic3d.2016.7823446

The effects of depth warping on perceived acceleration in stereoscopic animation

2016· article· en· W2576140020 on OpenAlexafffund
Sidrah Laldin, Laurie M. Wilcox, Robert S. Allison

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldEngineering
TopicAdvanced Optical Imaging Technologies
Canadian institutionsYork University
FundersOntario Centres of Excellence
KeywordsStereoscopyParallaxComputer visionArtificial intelligenceComputer scienceDepth perceptionAccelerationImage warpingBinocular disparityComputer graphics (images)PerceptionPhysics

Abstract

fetched live from OpenAlex

Stereoscopic media produce the sensation of depth through differences between the images presented to the two eyes. These differences arise from binocular parallax which in turn is caused by the separation of the cameras used to capture the scene. Creators of stereoscopic media face the challenge of depicting compelling depth while restricting the amount of parallax to a comfortable range. To address this tradeoff, stereoscopic warping or depth adjustment algorithms are used in the post-production process to selectively increase or decrease the depth in specific regions. This process modifies the image's depth-to-parallax mapping to suit the desired parallax range. As the depth is adjusted using non-linear parallax re-mapping functions, the geometric stereoscopic space is distorted. In addition, the relative expansion or compression of stereoscopic space should theoretically affect the perceived acceleration of an object passing through that region. Here we evaluate this prediction and determine if stereoscopic warping affects viewers' perception of acceleration. Observers judged the perceived acceleration of an approaching object (a toy helicopter) moving in depth through a complex stereoscopic 3D scene. The helicopter flew at one of two altitudes, either ground level or camera level. For each altitude, stereoscopic animations were produced under three depth re-mapping conditions (i) compressive, (ii) expansive, and (iii) zero (no re-mapping) for a total of six test conditions. We predicted that expansive depth re-mapping would produce a bias toward perceiving deceleration of the approaching helicopter, while compressive depth remapping would result in a bias toward seeing acceleration. However, there were no significant differences in the amount or direction of bias between the re-mapping conditions. We did find a significant effect of the helicopter altitude, such that there was little bias in acceleration judgements when the helicopter moved at ground level but a significant bias towards reporting acceleration when the helicopter moved at camera level. This result is consistent with the proposal that observers can make use of additional monocular (2D) cues in the ground level condition to improve their acceleration estimates. The lack of an effect of depth re-mapping suggests that viewers have considerable tolerance to depth distortions resulting from stereoscopic post-processing. These results have important implications for effective post-production and quality assurance for stereoscopic 3D content creation.

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: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.242
Threshold uncertainty score0.110

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 head0.010
GPT teacher head0.240
Teacher spread0.230 · 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 designBench or experimental
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
Published2016
Admission routes2
Has abstractyes

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