Depth constancy for virtual and physical objects
Bibliographic record
Abstract
It is generally assumed that stereopsis plays an important role in 3D shape perception; if so, the disparity-defined shape signal must be consistent across a range of viewing distances. Studies of stereoscopic depth constancy have used a wide variety of stimuli and tasks, and come to similarly wide-ranging conclusions. The aim of this series of experiments is to understand how perceived depth magnitude is impacted by viewing distance, cue conflicts, and surface structure in virtual and physical objects. To this end, we measured perceived depth magnitude using virtual textured half-cylinders and identical 3D printed versions, presented at 83 and 130cm. Virtual stimuli were viewed using a mirror stereoscope and an Oculus Rift head-mounted display. The physical stimuli were viewed in a controlled environment under similar lighting conditions. In all cases, observers used a pressure-sensitive strip to indicate the maximum depth of the cylinder, with stereopsis and without (monocular). Depth estimates were similar in the two virtual viewing conditions, despite the optical distortions and lower resolution of the VR display. Performance was more accurate when viewing physical objects. In all three conditions there was incomplete scaling of depth with viewing distance, but this was less extreme in the physical test condition. To estimate the 'assumed' distance to the object, we used each observers' estimate, binocular geometry, and maximum likelihood estimation. Comparison of the ratio of the presented distances to those computed from the results, suggests that distance was underestimated by about 22% in both the VR and stereoscope conditions. We conclude that the failure of depth constancy in virtual stimuli is not modulated by the degree of vergence-accommodation conflict, and that depth constancy is not complete, even for physical stimuli. Meeting abstract presented at VSS 2018
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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 | 0.004 |
| 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 | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".