Multiple range imaging camera operation with minimal performance impact
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
Time-of-flight range imaging cameras operate by illuminating a scene with amplitude modulated light and measuring the phase shift of the modulation envelope between the emitted and reflected light. Object distance can then be calculated from this phase measurement. This approach does not work in multiple camera environments as the measured phase is corrupted by the illumination from other cameras. To minimize inaccuracies in multiple camera environments, replacing the traditional cyclic modulation with pseudo-noise amplitude modulation has been previously demonstrated. However, this technique effectively reduced the modulation frequency, therefore decreasing the distance measurement precision (which has a proportional relationship with the modulation frequency). A new modulation scheme using maximum length pseudo-random sequences binary phase encoded onto the existing cyclic amplitude modulation, is presented. The effective modulation frequency therefore remains unchanged, providing range measurements with high precision. The effectiveness of the new modulation scheme was verified using a custom time-of-flight camera based on the PMD19-K2 range imaging sensor. The new pseudo-noise modulation has no significant performance decrease in a single camera environment. In a two camera environment, the precision is only reduced by the increased photon shot noise from the second illumination source.
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 | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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