Object Centered Stereo: Displacement Map Estimation Using Texture and Shading
Bibliographic record
Abstract
We consider the problem of recovering 3D surface displacements using both shading and multi-view stereo cues. In contrast to traditional disparity or depth map representations, the object centered displacement map representation enables the recovery of complete 3D objects while also ensuring the reconstruction is not biased towards a particular image. Although displacement mapping requires a base surface, this base mesh is easily obtained using traditional computer vision techniques (e.g., shape-from-silhouette or structure-from-motion). Our method exploits shading variation due to object rotation relative to the light source, allowing the recovery of displacements in both textured and textureless regions in a common framework. In particular, shading cues are integrated into a multi-view stereo photo-consistency function through the surface normals that are implied by the displacement map. The analytic gradient of this photo-consistency function is used to drive a multi-resolution conjugate gradient optimization. We demonstrate the geometric quality of the reconstructed displacements on several example objects including a human face.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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".