Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point cloud classification. SMCNet combines multi-view projection and neural radiance fields (NeRFs) to generate high-fidelity 2D representations with enhanced texture realism, addressing occlusions and lighting inconsistencies effectively. The Mamba model is further refined within this framework by integrating a depth perception module to capture long-range point interactions and adopting a dual-channel structure to enhance point-wise feature extraction. Fine-tuning adapters for the CLIP and Mamba models are also introduced, significantly improving cross-domain adaptability. Additionally, an intelligent voting mechanism aggregates predictions from multiple viewpoints, ensuring enhanced classification robustness. Comprehensive experiments demonstrate that SMCNet achieves state-of-the-art performance, outperforming the PointNet++ baseline with a 0.5% improvement in mean overall accuracy (mOA) on ModelNet40 and a 7.9% improvement on ScanObjectNN. In corruption resistance, SMCNet reduces the mean corruption error (mCE) by 0.8% on ModelNet40-C and 3.6% on ScanObjectNN-C. These results highlight the effectiveness of SMCNet in tackling real-world classification scenarios with noisy and corrupted data.
Accurate classification of three-dimensional (3D) point clouds in real-world environments is often impeded by sensor noise, occlusions, and incomplete data. To overcome these challenges, we propose SMCNet, a robust multimodal framework for 3D point cloud classification. SMCNet combines multi-view projection and neural radiance fields (NeRFs) to generate high-fidelity 2D representations with enhanced texture realism, addressing occlusions and lighting inconsistencies effectively.
Objective: Rapid and efficient analysis of cancer has become a focus of research. Artificial intelligence can use histopathological data to quickly determine the cancer situation, but still faces challenges. For example, the convolutional network is limited by the local receptive field, human histopathological information is precious and difficult to be collected in large quantities, and cross-domain data is hard to be used to learn histopathological features.
