Novel‐View Synthesis of Outdoor Sport Events Using an Adaptive View‐Dependent Geometry
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
Abstract We propose a novel fully automatic method for novel‐viewpoint synthesis. Our method robustly handles multi‐camera setups featuring wide‐baselines in an uncontrolled environment. In a first step, robust and sparse point correspondences are found based on an extension of the Daisy features [ TLF10 ]. These correspondences together with back‐projection errors are used to drive a novel adaptive coarse to fine reconstruction method, allowing to approximate detailed geometry while avoiding an extreme triangle count. To render the scene from arbitrary viewpoints we use a view‐dependent blending of color information in combination with a view‐dependent geometry morph. The view‐dependent geometry compensates for misalignments caused by calibration errors. We demonstrate that our method works well under arbitrary lighting conditions with as little as two cameras featuring wide‐baselines. The footage taken from real sports broadcast events contains fine geometric structures, which result in nice novel‐viewpoint renderings despite of the low resolution in the images.
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.
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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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 it