Bibliographic record
Abstract
Image-Based Rendering (IBR) has become widely known by its relatively low requirements for generating new scenes based on a sequence of reference images. This characteristic of IBR shows a remarkable potential impact in rendering complex 3D virtual environments on graphics-constrained devices, such as head-mounted displays, set-top boxes, media streaming devices, and so on. If well exploited, IBR coupled with remote rendering would enable the exploration of complex virtual environments on these devices. However, remote rendering requires the transmission of a large volume of images. In addition, existing solutions consider limited and/or deterministic navigation schemes as a means of decreasing the volume of streamed data. This article proposes the PRO gressive PAN orama Str E aming protocol (PROPANE) to offer users a smoother virtual navigation experience by prestreaming the imagery data required to generate new views as the user wanders within a 3D environment. PROPANE is based on a very simple yet effective trigonometry model and uses a strafe (lateral movement) technique to minimize the delay between image updates at the client end. This article introduces the concept of key partial panoramas, namely panorama segments that cover movements in any direction by simply strafing from an appropriate key partial panorama and streaming the amount of lost pixels. Therefore, PROPANE can provide a constrained device with sufficient imagery data to cover a future user's viewpoints, thereby minimizing the impact of transmission delay and jitter. PROPANE has been implemented and compared to two baseline remote rendering schemes. The evaluation results show that the proposed technique outperforms the selected and closely related existing schemes by minimizing the response time while not limiting the user to predefined paths as opposed to previous protocols.
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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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.140 | 0.067 |
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".