Collaborative Three-Dimensional Completion of Color and Depth in a Specified Area With Superpixels
Bibliographic record
Abstract
A persistent problem in the three-dimensional (3-D) reconstruction technique is to eliminate blank areas in the 3-D map, which commonly emerges after removing undesired objects, such as dynamic targets or occluded areas. This task is challenging for it is difficult to acquire the coherence between color and depth information, which are both lost for each pixel in the target region. Moreover, creating novel artifacts also needs to be avoided during the completion process. To address these problems, in this paper, we propose a collaborative method to complete the lost area in the color image and its corresponding depth map. In the proposed method, an examplar-based image inpainting technique combined with planarity knowledge is adopted to iteratively repair the texture and structure information. The color completion process provides candidates for plane-fitting on segmented superpixels. The depth filling step extracts image areas requiring reinpainting, which is defined by their gradient differences. The final 3-D map can then be reconstructed from both completed color and depth images. Experiments on real-world scenes demonstrate the success of our method in the completion of 3-D maps.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| 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".