Acquisition of omnidirectional stereoscopic images and videos of dynamic scenes: a review
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
Different camera configurations to capture panoramic images and videos are commercially available today. However, capturing omnistereoscopic snapshots and videos of dynamic scenes is still an open problem. Several methods to produce stereoscopic panoramas have been proposed in the last decade, some of which were conceived in the realm of robot navigation and three-dimensional (3-D) structure acquisition. Even though some of these methods can estimate omnidirectional depth in real time, they were not conceived to render panoramic images for binocular human viewing. Alternatively, sequential acquisition methods, such as rotating image sensors, can produce remarkable stereoscopic panoramas, but they are unable to capture real-time events. Hence, there is a need for a panoramic camera to enable the consistent and correct stereoscopic rendering of the scene in every direction. Potential uses for a stereo panoramic camera with such characteristics are free-viewpoint 3-D TV and image-based stereoscopic telepresence, among others. A comparative study of the different cameras and methods to create stereoscopic panoramas of a scene, highlighting those that can be used for the real-time acquisition of imagery and video, is presented.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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