Real-Time Virtual Viewpoint Generation on the GPU for Scene Navigation
Bibliographic record
Abstract
In this paper we present a method for achieving real-time view interpolation in a virtual navigation application that uses a collection of pre-captured panoramic views as a representation of the environment. In this context, viewpoint interpolation is essential to achieve smooth and realistic viewpoint transition while the user is moving from one panorama to another. In this proposed approach, view interpolation is achieved by first computing the optical flow field between a pair of adjacent panoramas. This flow field can then be used by the view morphing algorithm to generate, on-the-fly, virtual viewpoints in-between existing views. Realistic interpolation is obtained by taking into account both scene geometry and color information. To achieve real-time viewpoint interpolation, a GPU implementation of the viewpoint interpolation algorithm has been developed. We ran our algorithm on multiple interior and exterior scenes and we were able to produce smooth and realistic viewpoint transitions by generating virtual views at a rate of more than 300 panoramas per second.
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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) | 0.000 | 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 | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".