Choosing rendering parameters for effective communication of 3D shape
Bibliographic record
Abstract
We conducted a series of perceptual experiments to assess the contributions of rendering parameters to the perception of the shape of three-dimensional objects. For the experiments, observers viewed graphically rendered displays consisting of pairs of rotating objects and judged whether their shapes were identical. For some pairs they were, while for other pairs they differed by varying amounts. We determined the accuracy of shape perception from these discrimination judgments. We provide background information for the operational definition of shape used throughout the experiments, as well as for the rendering factors under experimental investigation: occluding contour, smooth shading, and specular highlights. Following that, we describe a series of experiments. Experiment 1 demonstrated the effectiveness of our new technique for the exploration of perceptual issues related to graphic interfaces. An additional four experiments produced results concerning the effects of rendering parameters on the communication of 3D shape. Experiments 2 and 3 investigated the contributions of basic rendering conditions such as the presence of occluding contours and smooth surface shading. In Experiments 4 and 5, the manipulation of specular highlighting revealed that accurate shape discrimination judgments were possible either with or without the specular component. These results lay a foundation for reasoned manipulation of interface properties when accurate communication of 3D shape is a primary goal of the display.
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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 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".