Bibliographic record
Abstract
This paper presents Rich360, a novel system for creating and viewing a 360° panoramic video obtained from multiple cameras placed on a structured rig. Rich360 provides an as-rich-as-possible 360° viewing experience by effectively resolving two issues that occur in the existing pipeline. First, a deformable spherical projection surface is utilized to minimize the parallax from multiple cameras. The surface is deformed spatio-temporally according to the depth constraints estimated from the overlapping video regions. This enables fast and efficient parallax-free stitching independent of the number of views. Next, a non-uniform spherical ray sampling is performed. The density of the sampling varies depending on the importance of the image region. Finally, for interactive viewing, the non-uniformly sampled video is mapped onto a uniform viewing sphere using a UV map. This approach can preserve the richness of the input videos when the resolution of the final 360° panoramic video is smaller than the overall resolution of the input videos, which is the case for most 360° panoramic videos. We show various results from Rich360 to demonstrate the richness of the output video and the advancement in the stitching results.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.059 | 0.017 |
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".