A Protocol for Interactive Streaming of Image-Based Scenes over Wireless Ad-hoc Networks
Bibliographic record
Abstract
Mobile computing devices with communication capabilities such as PDAs and cell phones are very popular and can be used as an ad-hoc network to interconnect users and services. The visualization of 3D scenes has many interesting applications, for instance, virtual guide, virtual mall, gaming, training, and monitoring. In this paper we focus on how to merge these technologies to stream interactive virtual environments to remote thin clients over ad-hoc networks, while minimizing the overhead. Mobile ad hoc networks poses significant challenges to multimedia streaming, mainly due to the mobility induced changes in bandwidth. We propose a protocol for interactive streaming of image-based rendered scenes which, different from the traditional geometry rendering schemes, is based on images to render photo-realistic views, that proved to be very suitable for such mobile devices. We also propose an image-based representation of 3D scenes and its packetization scheme, and a feedback mechanism to adapt the traffic to the frequent changes in bandwidth, and thus improving the performance of our streaming system. Besides presenting our proposed interactive streaming system, we also discuss the results of the simulation experiments.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".