Fast integration of depth from motion parallax and the effect of dynamic perspective cues
Bibliographic record
Abstract
In everyday life, we perceive depth relationships in a scene seemingly effortlessly and almost instantaneously. However, past experimental studies on motion parallax and structure from motion have reported that integration times of 600-1000 msec are required for perception of depth or 3D structure. Here we re-examined the temporal characteristics of depth discrimination from motion parallax, using random dot textured surfaces. Relative shearing motions of textures were synchronized to the observer's head movements to portray a surface slanted about a horizontal axis. The dot displacements were produced under two different rendering schemes: orthographic and perspective. Perspective rendering differs from orthographic by including additional cues, i.e. variation of speed of the dots with distance, lateral speed gradients across the display and small vertical displacements. No pictorial depth cues or variation of the size of the random dots with distance were available, and thus the task was impossible without observer movement. Three observers performed a 2AFC depth discrimination task in which they reported the perceived direction of slant, and the presentation duration of the stimulus was varied over intervals ranging from 62.5 to 4000 msec. The stimuli were presented on a computer screen in a 28 degree diameter circular window, at 57 cm viewing distance. We found that 1) better performance occurred with perspective than orthographic rendering at all stimulus presentation durations giving above chance performance; 2) subjects were able to discriminate depth at durations as short as 125 msec; 3) performance for both types of rendering was relatively constant for durations over 500 msec, but dropped at shorter durations. Somewhat surprisingly, the integration of dynamic perspective cues does not seem to require additional processing time. Depth from motion parallax can occur much more rapidly than previously thought, consistent with the apparent swiftness of depth perception that is experienced in everyday life. Meeting abstract presented at VSS 2016
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".