OccCasNet: Occlusion-Aware Cascade Cost Volume for Light Field Depth Estimation
Bibliographic record
Abstract
Depth estimation using the Light Field (LF) technique is an essential task with a wide range of practical applications. While mainstream approaches based on multi-view stereo techniques can attain exceptional accuracy by creating finer cost volumes, they are resource-intensive, time-consuming, and often overlook occlusion during cost volume construction. To address these issues and strike a better balance between accuracy and efficiency, we propose an occlusion-aware cascade cost volume for LF depth (disparity) estimation. Our cascaded strategy reduces the sampling number while maintaining a constant sampling interval, enabling the construction of a finer cost volume. We also introduce occlusion maps to enhance accuracy in constructing the occlusion-aware cost volume. Specifically, we first generate a coarse disparity map through a coarse disparity estimation network. Then, we warp the sub-aperture images (SAIs) of adjacent views to the center view based on the coarse disparity map to generate occlusion maps for each SAI by photo-consistency constraints. Finally, we seamlessly incorporate occlusion maps into cascade cost volume to construct an occlusion-aware refined cost volume, allowing the refined disparity estimation network to yield a more precise disparity map. Extensive experiments demonstrate the effectiveness of our method. Compared with the state-of-the-art techniques, our method achieves a superior balance between accuracy and efficiency, ranking first in the Q25 metric on the HCI 4D benchmark.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".