Remote rendering and streaming of progressive panoramas for mobile devices
Bibliographic record
Abstract
Providing mobile devices with virtual environment walkthrough and real-time streaming movie playback capability is expected to have a profound impact to the entertainment-based applications, such as virtual guides, online gaming, and e-learning, just to name a few. However, it is well known that it is extremely difficult to render complex 3D scenes at interactive frame rates on thin mobile devices known for their lack of proper resources needed to process large volume of 3D virtual environment data. In order to provide virtual environment navigation on thin mobile clients, we propose a hybrid technique which combines both remote geometry rendering and streaming of warped images. In our approach, the server renders a partial panoramic view, which is based on the user's viewpoint and last movements. The server then warps the image's coordinates into cylindrical coordinates, and streams the images to the client device, which will progressively build the panoramic representation of the scene. Furthermore, in order to enhance streaming performance and quality of the interaction, we propose to use a rate control mechanism as well as a prediction of the user's movements within the virtual scene. In this paper we discuss our scheme for remote rendering and streaming of progressive panoramas for mobile devices, and present our experimental results we have obtained in order to validate our proposed technique. Our results indicate clearly that the proposed solution is able to achieve stable frame rates and throughput in error-prone wireless channels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.003 | 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".