Multi-projectors for arbitrary surfaces without explicit calibration nor reconstruction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
We present a new approach allowing one or more projectors to display an undistorted image on a surface of unknown geometry. To achieve this, a single camera is used to capture the viewer's perspective of the projection surface. No explicit camera and projector calibration is required since only their relative geometries are computed using structured light patterns. There is no specific constraint on the position or the orientation of the projectors and the camera with respect to the projection surface, except that the area visible to the camera must be covered by the projectors. The procedure defines a function establishing the correspondence of each pixel of a projector image to a pixel of the camera image. After the mapping of each projector has been carried out, one can display an image corrected in real-time for the point of view of an observer, which takes into account his position, the surface distortion, and the projector position and orientation. This method automatically takes into account any distortion in the projector lenses. Typical applications of this method include projection in small rooms, shadow elimination and wide screen projection using multiple projectors. Intensity blending can be combined to our method to ensure minimal visual artifacts. The implementation has shown convincing results for many configurations.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it