A Tele-immersive System Based On Binocular View Interpolation
Bibliographic record
Abstract
The main idea behind tele-immersive environment is to create an immersive virtual environment that connect people across networks and enable them to interact not only with each other, but also with various other forms of shared digital data (video, 3D models, images, text, etc.). Tele-immersive environments may eventually replace current video and telephone conferencing, and enable for a better and more intuitive way to communicate between people and computer systems. To accomplish this, participants to a meeting has to be represented digitally with a high degree of accuracy in order to keep a sense of immersion. Tele-immersive environments should have the same "feel" as a real meeting. Interactions among people should be natural. In other to create such a system, we need to solve the key problem of how to create in real-time new views from a fixed network of cameras that will correspond to new viewpoints. We also need to do this for two virtual cameras corresponding to the inter-ocular distance of each participant. In this paper, we will describe a new binocular view interpolation method based on a re-projection technique using calibrated cameras. We will discuss the various aspects of this new algorithm and of the hardware systems necessary to perform these operations in real-time. We will also present early experimental results illustrating the various advantages of this algorithm.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".