Object reconstruction and pose indexing by volume feedback
Bibliographic record
Abstract
Three dimensional reconstruction of a rigid object from monocular video sequences is addressed. Initially object pose is estimated in each image by locating similar (unknown) textures assuming flat depth maps for all input images. Shape-from-silhouette Szeliski (1993) is then applied to make a 3-D model (volume), which is used for a new round of pose estimation, this time by a model-based method giving better estimates. Before repeating this process by building a new volume, pose estimates are adjusted to reduce error by maximizing a quality measure for shape-from-silhouette volume reconstruction. The volume feedback is terminated when pose estimates do not change much as compared to those produced by previous iteration. The final output is a pose index (the last set of pose estimates) and a volume. Good performance of the system is shown by several experiments. No model is assumed for the object. Feature points are neither detected nor tracked: no problematic feature matching or correspondence. The high-level pose index generated for input images can be used for content-based retrieval. Our method can be also applied to 3-D object tracking in video.
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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 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".