A Peer-to-Peer Approach for Remote Rendering and Image Streaming in Walkthrough Applications
Bibliographic record
Abstract
Motivated by the widespread of file and video streaming over peer-to-peer networks, we propose to investigate the design of a peer-to-peer solution for image-based remote walkthrough. Due to the fact that our scheme relies on images to provide the user with interactive virtual environments, even thin mobile devices can benefit from image-based rendering's lower graphics power demand when compared to geometry rendering. The main objective of this paper is to present our remote walk through system and discuss a simple peer-to-peer distribution algorithm. Similar to peer-to-peer networked virtual environment solutions, our approach involves discovering neighboring peers using region of interest, instead of proximity of peer nodes. The main idea is that closer virtual users will have higher probability for sharing images, as they are navigating in the same region. We also discuss our previous buffering and scheduling mechanisms and how they work in a peer-to-peer environment. We also discuss our simulation experiments in order to evaluate the performance of our approach.
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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 | 0.000 |
| 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".