PV-PASBLS: A Multimodal Point-View Fusion Model Based on Parameter Adaptive Stacked Broad Learning System for 3-D Shape Recognition
Bibliographic record
Abstract
Most existing multimodal point-view fusion models for 3-D shape recognition typically improve recognition accuracy through complex feature fusion mechanisms. However, these mechanisms significantly increase the model’s complexity and computational cost. To address this issue, a novel multimodal point-view fusion model based on a parameter adaptive stacked broad learning system (PV-PASBLS) for 3-D shape recognition is proposed. This model avoids complex feature fusion mechanisms, thereby reducing computational cost and increasing flexibility. Specifically, PV-PASBLS employs a backbone network for point cloud and multiview feature extraction to effectively capture the relevant features. Simple concatenation is then used for feature fusion. Importantly, PV-PASBLS is adaptive, allowing the backbone network to be replaced or adjusted to meet specific task requirements. Since PV-PASBLS avoids complex fusion mechanisms, the responsibility for achieving high-accuracy recognition is shifted to its classification network. To achieve this, a novel classification network, named the parameter adaptive stacked broad learning system (PASBLS), is proposed. PASBLS utilizes a new interval adaptive hyperparameter optimization (IAHPO) algorithm based on MARS. By constructing a surrogate model for a stacked broad learning system (SBLS) in PASBLS, IAHPO can quickly and efficiently identify the optimal hyperparameters, ensuring that PV-PASBLS maintains high recognition accuracy and mitigates potential accuracy losses due to the absence of complex fusion mechanisms. To validate the effectiveness of PV-PASBLS, comprehensive experiments were conducted on the public 3-D shape datasets ModelNet40 and ScanObjectNN, comparing it with state-of-the-art methods. Experimental results demonstrate that PV-PASBLS outperforms its competitors, achieving higher accuracy and improved training efficiency. In addition, the IAHPO algorithm was evaluated on the NORB classification dataset and ten UCI regression datasets, showing that it can achieve better hyperparameters with a lower computational cost.
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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".