Shape perception of thin transparent objects with stereoscopic viewing
Bibliographic record
Abstract
Many materials, including water surfaces, jewels, and glassware exhibit transparent refractions. The human visual system can somehow recover 3D shape from refracted images. While previous research has elucidated various visual cues that can facilitate visual perception of transparent objects, most of them focused on monocular material perception. The question of shape perception of transparent objects is much more complex and few studies have been undertaken, particular in terms of binocular vision. In this article, we first design a system for stereoscopic surface orientation estimation with photo-realistic stimuli. It displays pre-rendered stereoscopic images and a real-time S3D (Stereoscopic 3D) shape probe simultaneously. Then we estimate people's perception of the shape of thin transparent objects using a gauge figure task. Our results suggest that people can consistently perceive the surface orientation of thin transparent objects, and stereoscopic viewing improves the precision of estimates. To explain the results, we present an edge-aware orientation map based on image gradients and structure tensors to illustrate the orientation information in images. We also decomposed the normal direction of the surface into azimuth angle and slant angle to explain why additional depth information can improve the accuracy of perceived normal direction.
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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 | 0.001 |
| 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) | 0.001 | 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".