Design of Binocular Stereo Vision System Via CNN-based Stereo Matching Algorithm
Bibliographic record
Abstract
In this paper, we design a binocular stereo vision system based on an adjustable narrow-baseline stereo camera for extracting depth information from a rectified stereo pair. The camera calibration and rectification are performed to get a rectified stereo pair serving as the input to the stereo matching algorithm. This algorithm searches the corresponding points between the left and right images and produces a disparity map that is used to obtain the depths via the triangulation principle. We focus on the first stage of the algorithm and propose a CNN-based approach to calculating the matching cost. Fast and slow networks are presented and trained on standard stereo datasets. The output of either network is regarded as the initial matching cost, followed by a series of post-processing methods for generating qualified disparity maps. The contrast tests have demonstrated that the CNN-based methods outperform census transformation on the mentioned datasets. Finally, we advance two error criteria to acquire the range of system working distance under diverse baseline lengths.
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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".