Occlusion-Aware Self-Supervised Stereo Matching with Confidence Guided Raw Disparity Fusion
Bibliographic record
Abstract
Commercially available stereo cameras used in robots and other intelligent systems to obtain depth information typically rely on traditional stereo matching algorithms. Although their raw (predicted) disparity maps contain incorrect estimates, these algorithms can still provide useful prior information towards more accurate prediction. We propose a pipeline to incorporate this prior information to produce more accurate disparity maps. The proposed pipeline includes a confidence generation component to identify raw disparity inaccuracies as well as a self-supervised deep neural network (DNN) to predict disparity and compute the corresponding occlusion masks. The proposed DNN consists of a feature extraction module, a confidence guided raw disparity fusion module to generate an initial disparity map, and a hierarchical occlusion-aware disparity refinement module to compute the final estimates. Experimental results on public datasets verify that the proposed pipeline has competitive accuracy with real-time processing rate. We also test the pipeline with images captured by commercial stereo cameras to show its effectiveness in improving their raw disparity estimates.
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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 | 0.002 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".