Global Color Consistency Correction for Large-Scale Images in 3-D Reconstruction
Bibliographic record
Abstract
Global color consistency correction for multiview images in three-dimensional (3-D) reconstruction is an important problem. The color differences between the images will affect the result of dense matching, thereby reducing the geometric accuracy of the mesh model. Moreover, it will also affect the result of texture mapping, causing color differences in the textured model. The color correction method based on global optimization is mainly used to solve this problem. And existing methods usually use sparse matching points as the color correspondences, but the correction results are not accurate enough as a result of the sparsity of the points. Besides, their efficiency of solving large-scale images globally is low. This article proposes a novel color correction method to eliminate the color differences between large-scale multiview images effectively. The core idea of our method is to group images by graph partition algorithm, and then perform intragroup correction and intergroup correction in sequence. First, for each pair of images, we calculate the reliable matching regions around the sparse points as the color correspondences according to the local homography principle. Compared with sparse matching points, our strategy can achieve more accurate color correction results. Next, for large-scale images, we partition them into many groups. For each group of images, the correction parameters are solved to eliminate the color differences of the images included in the group. Finally, we eliminate the color differences between groups by intergroup correction to achieve overall color consistency. Experimental results on typical datasets demonstrate that the proposed method is better than the current representative methods. The proposed method shows better color consistency in the extreme cases, and also exhibits higher computational efficiency on large-scale image sets.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".