Deep Learning-Driven Depth from Defocus via Active Multispectral Quasi-Random Projections with Complex Subpatterns
Bibliographic record
Abstract
A promising approach to depth from defocus (DfD) involves actively projecting a quasi-random point pattern onto an object and assessing the blurriness of the point projection as captured by a camera to recover the depth of the scene. Recently, it was found that the depth inference can be made not only faster but also more accurate by leveraging deep learning approaches to computationally model and predict depth based on the quasi-random point projections as captured by a camera. Motivated by the fact that deep learning techniques can automatically learn useful features from the captured image of the projection, in this paper we present an extension of this quasi-random projection approach to DfD by introducing the use of a new quasi-random projection pattern consisting of complex subpatterns instead of points. The design and choice of the subpattern used in the quasi-random projection is a key factor in the ability to achieve improved depth recovery with high fidelity. Experimental results using quasi-random projection patterns composed of a variety of non-conventional subpattern designs on complex surfaces showed that the use of complex subpatterns in the quasi-random projection pattern can significantly improve depth reconstruction quality compared to a point pattern.
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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 | 0.000 |
| 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".