An Evaluation of Depth and Size Perception on a Spherical Fish Tank Virtual Reality Display
Why this work is in the frame
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Bibliographic record
Abstract
Fish Tank Virtual Reality (FTVR) displays create a compelling 3D spatial effect by rendering to the perspective of the viewer with head-tracking. Combining FTVR with a spherical display enhances the 3D experience with unique properties of the spherical screen such as the enclosing shape, consistent curved surface, and borderless views from all angles around the display. The ability to generate a strong 3D effect on a spherical display with head-tracked rendering is promising for increasing user's performance in 3D tasks. An unanswered question is whether these natural affordances of spherical FTVR displays can improve spatial perception in comparison to traditional flat FTVR displays. To investigate this question, we conducted an experiment to see whether users can perceive the depth and size of virtual objects better on a spherical FTVR display compared to a flat FTVR display on two tasks. Using the spherical display, we found significantly that users had 1cm depth accuracy compared to 6.5cm accuracy using the flat display on a depth-ranking task. Likewise, their performance on a size-matching task was also significantly better with the size error of 2.3mm on the spherical display compared to 3.1mm on the flat display. Furthermore, the perception of size-constancy is stronger on the spherical display than the flat display. This study indicates that the natural affordances provided by the spherical form factor improve depth and size perception in 3D compared to a flat display. We believe that spherical FTVR displays have potential as a 3D virtual environment to provide better task performance for various 3D applications such as 3D designs, scientific visualizations, and virtual surgery.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it