Expanding Disparity Range in an FPGA Stereo System While Keeping Resource Utilization Low
Bibliographic record
Abstract
Field Programmable Gate Array devices (FPGAs) are attractive for implementing computer vision algorithms due to the fact that they allow the designer to exploit the parallel nature of many vision algorithms, thus offering substantial speed improvements over fixed hardware/software systems. One major drawback, however, is that adding resources to reconfigurable devices is not as simple as adding memory or upgrading a hard disk. This becomes a problem when adapting a system to deal with larger image sizes or, in the case of stereo disparity estimation, larger disparity ranges (parameter ranges in general). This paper describes preliminary work on a design to expand the disparity range and input image size of an existing FPGA-based stereo system [8] while not significantly expanding resource requirements. Simulations on synthetic and real images are given, as well as a description of the proposed architecture changes in porting the system to a newer FPGA architecture.
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How this classification was reachedexpand
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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".