An efficient object discovery and selection protocol in 3D streaming-based systems over thin mobile devices
Why this work is in the frame
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Bibliographic record
Abstract
3D streaming over thin mobile devices is considered challenging due to the mobile devices' limited capabilities such as limited energy lifetime, processing power, storage capacity, and graphics' hardware and accelerator capabilities, that make it very difficult for mobile devices to render and process large and complex 3D scenes. To address this issue, 3D streaming techniques have been proposed that aim at reducing the resolution of 3D objects to make it easy to deliver and render. However, streaming all of the 3D objects in a given virtual environment (VE) is not considered efficient. In this paper, we propose a novel object selection technique for 3D streaming based systems over thin mobile devices, which we refer to as OCTET. Our protocol, which is based on multi-level area of interest (AOI), allows for the selection of the 3D objects required for the user's virtual scene, taking into account the user's interests, the location of the 3D objects in the multi-level AOI, and the mobile device's available resources.
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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 | 0.000 | 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 | 0.001 | 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