Re-parameterization of a Deformation Model for Non-rigid Registration
Bibliographic record
Abstract
In this paper we present a method for non-rigid registration of meshes. The method aligns two surfaces of deformable objects by automatically separating the deformation into a single global transformation and other local deformations. The local deformations are found by applying the deformation model proposed by Sumner (Sumner et al., 2007) that previous methods have used to register two surfaces. However, we specify a rigid transformation for each node of the deformation graph using a rotation matrix and a translation vector. With this model, unit quaternions of rotation matrices are paramterized using an homeomorphic relation between the 4D unit sphere and the 3D projective space. Therefore, the number of unknowns is reduced by half compared to the original models based on affine transformations and the optimization process is less complex. We demonstrate the efficiency of the proposed method by aligning the surfaces of data sets without any prior knowledge and assumptions about the deformation between the two surfaces.
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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) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.005 | 0.004 |
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".