Integration of motion and disparity in reconstructing 3D surface shape
Bibliographic record
Abstract
There is convincing evidence that the visual system acts as an optimal integrator when combining texture with disparity or motion to interpret 3D surface shape. Unfortunately, similar experiments with motion and disparity have proven challenging due to the presence of additional shape cues. Our experiments address these stimulus issues and evaluate how stereo and motion cues are combined to resolve 3D form. Three-dimensional cylinders were covered with a random-element greyscale texture. A black occluder with randomly positioned, 1.5 degree circular holes was placed in front of the display to limit observers' ability to track local features, or extract shape from texture. Using an implicit standard technique, with the method of constants, we assessed the accuracy and precision of observers' curvature discrimination judgments for a range of implicit reference curvatures (radii of 15, 16 and 17.50 deg). We did this first for motion and disparity alone, and in combination (equivalent and conflicting). In the combined conditions the relative strength of the disparity and motion cues was determined by selecting the 70% correct point from each individual psychometric function obtained in the single cue condition. Combined-equivalent results showed a marked increase in the slope of the psychometric function for all test curvatures. In the conflict conditions there were considerable individual differences in the weighting of the two cues. However, in all cases, there is support for cue integration rather than a vetoing process. These results and analyses will be discussed in the context of current models of cue integration.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".