PV-PASBLS: A Multimodal Point-View Fusion Model Based on Parameter Adaptive Stacked Broad Learning System for 3-D Shape Recognition

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.