Microfacet-based photometric stereo for surfaces with isotropic reflectance
Bibliographic record
Abstract
A precise, stable, and invertible model for surface reflectance is the key to the success of calibrated photometric stereo.Though models addressing low frequency reflectance have been proposed for a broad group of non-Lambertian surfaces, an effective solution directly targeting highly specular reflectance remains elusive.This thesis introduces an analytical isotropic microfacet-based reflectance model, based on which there is a physically interpretable approximation that can be tailored for highly specular surfaces.With this approximation, it shows that a surface recovery problem is essentially an ellipsoid of revolution fitting problem, and a fast, non-iterative and globally optimal solver is derived to attack the latter.Additionally, the introduced model also justifies the fact that, if specularity is not captured by any directional light, a very smooth surface can also appear to be diffusive.This fact leads to a design of an iterative solver for surface estimation with general isotropic reflectance, where the formulation handling specularity is taken as a special instance.Empirical results on images of both synthetic appearances and real objects can validate this model and demonstrate that the proposed solution can stably deliver the state-of-the-art performance.i My deep gratitude goes to my thesis adviser professor Michael Langer for his guidance on my thesis work and for opening the door for me to the joyful research experience in computer vision.The days I spent at School of Computer Science of McGill has made a significant impact on a then software engineer when he was in search of his career goal.My appreciation also extends to everyone I have worked with during
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| 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 | 0.002 |
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".