Adaptive online transmission of 3D TexMesh using scale-space analysis
Bibliographic record
Abstract
Efficient online visualization of 3D mesh and photo realistic texture is essential for a variety of applications, such as museum exhibits and medical images. In these applications synthetic texture and a predefined set of views is not an option. We propose using a mesh simplification algorithm based on scale-space analysis of the feature point distribution, combined with an associated analysis of the surface texture, to address the needs of adaptive online transmission of high quality 3D objects. The premise of the proposed textured mesh (TexMesh) simplification is the following: minor variations in texture can be ignored in relatively smooth regions of a 3D surface, without significantly affecting human perception. Statistics on 3D feature point distribution and their associated texture fragments are gathered during preprocessing. Online transmission is based on these statistics, which can be retrieved in constant time. Based on monitored bandwidth a scaled mesh is first transmitted. Starting from a default texture quality, we apply an efficient Harmonic Time Compensation Algorithm based on the current bandwidth and a time limit, to adaptively adjust the texture quality of the next fragment. Properties of the algorithm are proved. Experimental results show the usefulness of our approach.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".