Optimal Camera Parameters for Depth from Defocus
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Pictures taken with finite aperture lenses typically have out-of-focus regions. While such defocus blur is useful for creating photographic effects, it can also be used for depth estimation. In this paper, we look at different camera settings for Depth from Defocus (DFD), the conditions under which depth can be estimated unambiguously for those settings and optimality of different settings in terms of lower bound of error variance. We present results for general camera settings, as well as two of the most widely used camera settings namely, variable aperture and variable focus. We show that for variable focus, the range of depth needs to be larger than twice the focal length to unambiguously estimate depth. We analytically derive the optimal aperture ratio, and also show that there is no single optimal parameter for variable focus. Furthermore we show how to choose focus in order to minimize error variance in a particular region of the scene.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 it