Compact and fast depth sensor based on a liquid lens using chromatic aberration to improve accuracy
Bibliographic record
Abstract
Depth from defocus (DFD) obtains depth information using two defocused images, making it possible to obtain a depth map with high resolution equal to that of the RGB image. However, it is difficult to change the focus mechanically in real-time applications, and the depth range is narrow because it is inversely proportional to the depth accuracy. This paper presents a compact DFD system based on a liquid lens that uses chromatic aberration for real-time application and depth accuracy improvement. The electrical focus changing of a liquid lens greatly shortens the image-capturing time, making it suitable for real-time applications as well as helping with compact lens design. Depth accuracy can be improved by dividing the depth range into three channels using chromatic aberration. This work demonstrated the improvement of depth accuracy through theory and simulation and verified it through DFD system design and depth measurement experiments of real 3D objects. Our depth measurement system showed a root mean square error (RMSE) of 0.7 mm to 4.98 mm compared to 2.275 mm to 12.3 mm in the conventional method, for the depth measurement range of 30 cm to 70 cm. Only three lenses are required in the total optical system. The response time of changing focus by the liquid lens is 10 ms, so two defocused images for DFD can be acquired within a single frame period of real-time operations. Lens design and image processing were conducted using Zemax and MATLAB, respectively.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".