3D model recognition and segmentation based on multi-feature fusion

Current methods focusing on 3D model recognition and segmentation have to some extent ignored the relationship between the high-level global single-point features and the low-level local geometric features of those models, resulting in poor recognition results. A multi-feature fusion approach which takes into consideration the aforementioned ignored relationship is proposed. First, a global single-point network is established to extract the global single-point features with high-level semantic recognition ability by increasing both the width of convolution kernel and the depth of the network. Second, an attentional fusion layer is constructed to learn the implicit relationship between global single-point features and local geometric features to fully explore the fine-grained geometric features that can better represent model categories. Finally, the global single-point features and fine-grained geometric features are further fused to achieve the complementation of advantages and enhance the feature richness. Experimental verification is carried out on the 3D model recognition datasets ModelNet40, ModelNet10 and segmentation datasets ShapeNet Parts, S3DIS, vKITTI, respectively, and comparison with current mainstream recognition algorithms shows that the proposed algorithm not only has higher recognition and segmentation accuracy, but also has stronger robustness.