Investigation of the effect of three-dimensional smoothing on multiview stereo images
Bibliographic record
Abstract
Future stereoscopic (3D) systems will become multiview capable to allow for the user to experience a more realistic 3D experience since they will not be limited to one view. This will help to make 3D technology more realistic, however, viewing discomfort will still be an issue. When viewing stereoscopic images, one cause of viewing discomfort can be attributed to the images appearing unnaturally sharp across the entire range of depth. To correct this problem for multiview images, a 3D filtering approach is proposed that will reduce the computation time required since the filter need only be applied once, whereas conventional 2D filtering techniques would be required to be performed 2n times (where n is the number of views). After conducting an initial experiment on 15 people, the proposed filter (on average) received similar ratings for discomfort and naturalness, when compared to the well established 2D bilateral filters. The benefit of this work is that it can provide an alternative method for filtering multiview images at a low cost, while obtaining similar results to bilateral filters, making it a useful filter for a wide range of future multiview stereo systems/applications.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.002 | 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".