Dynamic programming approach to high frame-rate stereo correspondence: A pipelined architecture implemented on a field programmable gate array
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
Estimation of depth within an imaged scene can be formulated as a stereo correspondence problem. Typical software solutions tend to be too slow for high frame rate (i.e. ges 30 fps) performance. Equivalent hardware solutions, however, can result in marked improvements. This paper explores one such pipelined hardware implementation that generates dense binocular disparity (depth) estimates at frame rates of up to 200 fps or more. The architecture is based on a dynamic programming maximum likelihood (DPML) formulation developed by Cox et al. [1996]. A field programmable gate array (FPGA) implementation of this architecture demonstrates equivalent accuracy while executing at significantly higher frame rates. It is noted that the architecture holds potential for more generalized hardware implementations of dynamic programming solutions [W. James et al.].
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| 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