Name
Title | ||
|---|---|---|
Evaluating Initialization Methods for Discriminative and Fast-Converging HGMM Point Clouds |
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 scale-invariance across differently sized datasets, and iii) greater stability for different initial scales of covariance matrices of the HGMM. |
Year | DOI | Venue |
|---|---|---|
2021 | 10.1109/ICRA48506.2021.9560882 | 2021 IEEE INTERNATIONAL CONFERENCE ON ROBOTICS AND AUTOMATION (ICRA 2021) |
DocType | Volume | Issue |
Conference | 2021 | 1 |
ISSN | Citations | PageRank |
1050-4729 | 0 | 0.34 |
References | Authors | |
0 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haohan Lin | 1 | 0 | 0.34 |
| Xuzhan Chen | 2 | 0 | 2.37 |
| Matthew Tucsok | 3 | 0 | 0.34 |
| Li Ji | 4 | 0 | 0.34 |
| Homayoun Najjaran | 5 | 3 | 1.39 |