Remote rendering and streaming of progressive panoramas for mobile devices
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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