Depth-map and albedo estimation with superior information-theoretic performance
Bibliographic record
Abstract
Lambertian photometric stereo (PS) is a seminal computer vision method. However, using depth maps in the image formation model, instead of surface normals as in PS, reduces model parameters by a third, making it preferred from an information-theoretic perspective. The Akaike information criterion (AIC) quantifies this trade-off between goodness of fit and overfitting. Obtaining superior AIC values requires an effective maximum likelihood (ML) depth-map & albedo estimation method. Recently, the authors published an ML estimation method that uses a two-step approach based on PS. While effective, approximations of noise distributions and decoupling of depth-map & albedo estimation have limited its accuracy. Overcoming these limitations, this paper presents an ML method operating directly on images. The previous two-step ML method provides a robust initial solution, which kick starts a new nonlinear estimation process. An innovative formulation of the estimation task, including a separable nonlinear least-squares approach, reduces the computational burden of the optimization process. Experiments demonstrate visual improvements under noisy conditions by avoiding overfitting. As well, a comprehensive analysis shows that refined depth maps & albedos produce superior AIC metrics and enjoy better predictive accuracy than with literature methods. The results indicate that the new method is a promising means for depth-map & albedo estimation with superior information-theoretic performance.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".