Globally Consistent Space‐Time Reconstruction
Bibliographic record
Abstract
Abstract Most objects deform gradually over time, without abrupt changes in geometry or topology, such as changes in genus. Correct space‐time reconstruction of such objects should satisfy this gradual change prior. This requirement necessitates a globally consistent interpretation of spatial adjacency. Consider the capture of a surface that comes in contact with itself during the deformation process, such as a hand with different fingers touching one another in parts of the sequence. Naive reconstruction would glue the contact regions together for the duration of each contact and keep them apart in other parts of the sequence. However such reconstruction violates the gradual change prior as it enforces a drastic intrinsic change in the object's geometry at the transition between the glued and unglued sub‐sequences. Instead consistent global reconstruction should keep the surfaces separate throughout the entire sequence. We introduce a new method for globally consistent space‐time geometry and motion reconstruction from video capture. We use the gradual change prior to resolve inconsistencies and faithfully reconstruct the geometry and motion of the scanned objects. In contrast to most previous methods our algorithm doesn't require a strong shape prior such as a template and provides better results than other template‐free approaches.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".