Single-pixel three-dimensional dual photography
Bibliographic record
Abstract
We report single-pixel three-dimensional (3D) dual photography. Inspired by a tri-linkage between single-pixel imaging, fringe projection profilometry (FPP), and dual photography, we propose a method for 3D imaging that allows for the synthesis of dual and relit images in a camera-free context. By using FPP calibration methods and implementing a direct coordinate mapping with phase shifting, we obtain highly efficient measurements of the direct component of the scene light transport matrix, while simultaneously sensing the surface profiles of 3D objects. Through the addition of calibration information and by exploiting Helmholtz reciprocity, dual photography and scene relighting can thus be performed on 3D images. To verify the proposed imaging method, we have developed a light-path symmetric single-pixel imaging system based on two digital micromirror devices (DMDs). As a benefit of this choice of imaging platform, the traditionally distinct roles of projector and camera hardware can be freely interchanged, depending only on the DMD hardware chosen for the deployment of modulation patterns. For the acquisition of images, binary cyclic S-matrix patterns and binary sinusoidal fringe patterns are loaded onto each DMD for scene encoding and virtual fringe projection, respectively. Using this system, we further demonstrate the broadening of traditional dual photography image synthesis to viewing and relighting of 3D images at user-selectable perspectives. Our work extends the conceptual scope and the imaging capability of dual photography.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".