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 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 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".