Evaluating Initialization Methods for Discriminative and Fast-Converging HGMM Point Clouds
Bibliographic record
Abstract
Discriminative data representations for point cloud data are critical for computer vision applications. Recently, the Hierarchical Gaussian Mixture Model (HGMM) has become a popular representation due to its compactness and real-time execution. However, HGMM still lacks a well-designed and robust initialization criterion. Ad-hoc initializations for HGMM can lead to a low discriminative clustering capability, slow convergence, and loss of scale-invariance. To adopt the optimal initialization scheme, we evaluate four potential candidates: K-Means++, Fuzzy C-Means (FCM), uniform, and random initialization across a few synthetic and measured datasets. Our experiments involve comparing the quality of HGMM point cloud reconstruction based on different initialization methods. The reconstruction quality is evaluated by the peak signal-to-noise ratio (PSNR). Our experiments show that clustering-based initialization methods can result in higher-quality HGMMs because of i) faster convergence of the Expectation-Maximization (EM) optimization, ii) better scaleinvariance across differently sized datasets, and iii) greater stability for different initial scales of covariance matrices of the HGMM.
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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 | 0.005 | 0.019 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".