THE UNIVERSITY OF CALGARY 4D Light Field Processing and its Application to Computer Vision
Bibliographic record
Abstract
ii Light elds have been explored extensively as a means of quickly rendering images of 3-dimensional scenes from novel camera positions. Because a light eld models the light rays permeating a scene, rather than modelling the geometry of that scene, the process of rendering images from a light eld is fast, with a speed which is independent of scene complexity. The light eld itself is a 4-dimensional data structure, representing the values of the light rays permeating a scene as a function of their positions and directions. Because a light eld can be used to model a real-world scene, and because the resulting model contains a wealth of information about that scene, simple and robust techniques may be applied to light elds to accomplish complex tasks. This work develops methods of extracting useful information from light eld models of real-world scenes. In particular, techniques are developed for ltering light elds based on depth, and for estimating the geometry of the scenes that they
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.056 | 0.020 |
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".