Le cinema omnistereo ou l'art d'avoir des yeux tout le tour de la tete
Bibliographic record
Abstract
This thesis deals with aspects of shooting, projection and perception of stereo panoramic cinema, also called omnistereo cinema. It falls largely in the field of computer vision, but it also in the areas of computer graphics and human visual perception. Omnistereo cinema uses immersive screens to project videos that provide depth information of a scene all around the spectators. Many challenges remain in omnistereo cinema, in particular shooting omnistereo videos for dynamic scenes, polarized projection on highly reflective screens making difficult the process to recover their shape by active reconstruction, and perception of depth distortions introduced by omnistereo images. Our thesis addressed these challenges by making three major contributions. First, we developed the first mosaicing method of omnistereo videos for stochastic and localized motions. We developed a psychophysical experiment that shows the effectiveness of the method for scenes without isolated structure, such as water flows. We also propose a shooting method that adds to these videos foreground motions that are not as constrained, like a moving actor. Second, we introduced new light patterns that allow a camera and a projector to recover the shape of objects likely to produce interreflections. These patterns are general enough to not only recover the shape of omnistereo screens, but also very complex objects that have depth discontinuities from the viewpoint of the camera. Third, we showed that omnistereo distortions are negligible for a viewer located at the center of a cylindrical screen, as they are in the periphery of the visual field where the human visual system becomes less accurate. Keywords: cinema, omnistereo, immersion, panoramic, stereo, computer vision, visual perception, active reconstruction, psychophysic experiment.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.012 | 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".