3D Reconstruction by Fusioning Shadow and Silhouette Information
Bibliographic record
Abstract
In this paper, we propose a new 3D reconstruction method using mainly the shadow and silhouette information of a moving object or person. This method is derived from the well-known Shape From Silhouettes (SFS) approach. A light source can be seen as a camera, which generates an image as a silhouette shadow. Based on this, we propose to replace a multicamera system of SFS by multi-infrared light sources while keeping the same procedure of Visual Hull reconstruction (VH). Therefore, our system consists of infrared light sources and one infrared camera. In this case, in addition to the object silhouette given by the camera, each light source generates an object shadow that reveals the object. Thus, as in SFS, the VH of a given object is reconstructed by intersecting the visual cones. Our method has many advantages compared to SFS and preliminary results, on synthetic and real scene images, showed that the system could be applied in several contexts.
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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 | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".