3D Online Multimedia and Games
Bibliographic record
Abstract
Online applications have been gaining wide acceptance among the general public. Companies like Amazon, Google, Yahoo! and NetFlicks have been doing extremely well over the last few years largely because of people becoming more comfortable and trusting of the Internet. The increasing acceptance of online products makes it increasingly important to address some of the scientific techniques involved in developing efficient 3D online systems.\n\nThe topics discussed in this book broadly cover four categories: networking issues in online multimedia; joint texture-mesh simplification and view independent transmission; view dependent transmission and server-side rendering; content and background creation; and creating simple online games.\n\n \nContents:\nAdaptive Bandwidth Monitoring for QoS Based Retrievel (A Basu et al.)\nWireless Protocols (A Khan)\nOverview of 3D Coding and Simplification (I Cheng & L Ying)\nScale-Space Filtering and LOD — The TexMesh Model (I Cheng)\nAdaptive Online Transmission of Photo-Realistic Textured Mesh (I Cheng)\nPerceptual Issues in a 3D TexMesh Model (I Cheng)\nQuality Metric for Approximating Subjective Evaluation of 3D Objects (A Basu et al.)\nPerceptually Optimized 3D Transmission Over Wireless Networks (I Cheng & A Basu)\nPredictive Schemes for Remote Visualization of 3D Models (P Zanuttigh & G M Cortelazzo)\nA Rate Distortion Theoretic Approach to Remote Visualization of 3D Models (N Brusco et al.)\n3D Content Creation by Passive Optical Methods (L Ballan et al.)\n3D Visualization and Compression of Photorealistic Panoramic Backgrounds (P Zanuttigh et al.)\nA 3D Game — Castles (G Xiao et al.)\nA Networked Version of Castles (D Lien et al.)\nA Networked Multiplayer Java3D Game — Siege (E Benner et al.)\nCollaborative Online 3D Editing (I Cheng et al.)
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.064 | 0.014 |
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".