Generation of Spatial-Temporal Panoramas with a Single Moving Camera
Bibliographic record
Abstract
Development of image stitching techniques, which take multiple images and stitch them together to make natural looking panoramas, is an integral part of the new wave in visual media - the 360 surround displays, such as the Oculus Rift. However, as compared to static image stitching, there has been little research conducted on video stitching, especially methods of generating panoramic video content from a single moving video camera. In this paper we present an effective means of creating video panorama from a single camera. Our video stitching method allows for widening the field of view of a video while maintaining spatial-temporal consistency. In this approach, a single image warping transformation, or homography, is obtained by averaging several rotation matrices from several frames. The application of the same homography to every frame, with fixed camera intrinsic parameters, provides enhanced stability of the resulting panoramic video. This novel stitching method can be used for a variety of applications, such as allowing the creation of panoramic video with smartphones and enabling gaming and movie companies to make panoramic video content in a flexible and inexpensive manner.
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.
How this classification was reachedexpand
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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".