Multi-reference object pose indexing and 3-D modeling from video using volume feedback
Bibliographic record
Abstract
A system for 3-D reconstruction of a rigid object from monocular video sequences is introduced. Initially an object pose is estimated in each image by locating similar (unknown) texture assuming flat depth map for all images. Shape-from-silhouette as stated in R. Szeliski (1993) is then applied to construct a 3-D model which is used to obtain better pose estimates using a model-based method. Before repeating the process by building a new 3-D model, pose estimates are adjusted to reduce error by maximizing a quality measure for shape-from-silhouette volume reconstruction. Translation of the object in the input sequence is compensated in two stages. The volume feedback is terminated when the updates in pose estimates become small. The final output is a pose index (the last set of pose estimates) and a 3-D model of the object. Good performance of the system is shown by experiments on a real video sequence of a human head. Our method has the following advantages: (1) No model is assumed for the object. (2) Feature points are neither detected nor tracked, thus no problematic feature matching or lengthy point tracking are required. (3) The method generates a high level pose index for the input images, these can be used for content-based retrieval. Our method can also be 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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".