Distortion metric for robust 3D point cloud transmission
Why this work is in the frame
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Bibliographic record
Abstract
This paper discusses using a forward error correction (FEC) algorithm to protect the transmission of progressively compressed 3D point clouds against packets loss. We design a metric to evaluate each layer's quality contribution to the decoding result of the progressively compressed model. With this metric, we minimize the expected distortion when applying an Unequal Error Protection (UEP) strategy to allocate channel bits to different layers of the model. The performance of employing UEP and Equal Error Protection (EEP) are compared with respect to the expected distortion. Experimental results show that by incorporating our distortion estimation metric with UEP, the rendering quality of a reconstructed 3D model degrades more gracefully as the packet-loss rate increases.
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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 | 0.001 |
| 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