A new method for 3D object reconstruction in real-time
Bibliographic record
Abstract
A novel real-time depth-mapping principle and camera where pulsed laser light is combined with a gain-modulated camera and a phase-locked loop control of laser intensity is described in this paper. The depth resolution is variable depending on the resolution of the camera and of the gating possibilities of the sensor. A sensor of 1 Mpixel is used providing a resolution of 1024 × 1024 which can be gated with very high speeds up to a few ns. Front images of real objects are reconstructed in 3D views based on the data provided by the laser imaging technique and on a new image processing algorithm, in real-time. The new method based on pulsed laser diodes is applicable to various types of image sensors as required by the application domain. As such the camera can be used for gaming, for controlling through gestures various computer applications spanning from digital signage to for example unmanned vehicles. Results are provided for a low end camera used in gaming. A new human computer interface based on gesture control is described. A series of experiments in which the camera is used to capture human gestures which are interpreted and recognized by various image processing algorithms are given.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".