PointFusionNet: Point feature fusion network for 3D point clouds analysis

The 3D point clouds is an important type of geometric data structure, and the analysis of 3D point clouds based on deep learning is a very challenging task due to the disorder and irregularity. In existing research, RS-CNN provides an effective and promising method to obtain shape features on disordered point clouds directly, which encodes local features effectively. However, RS-CNN fails to consider point-wise features and global features, which are conducive to point clouds better. In this paper, we proposed PointFusionNet, which solves these problems effectively by fusing point-wise features, local features, and global features. We have designed Feature Fusion Convolution (FF-Conv) and Global Relationship Reasoning Module (GRRM) to build PointFusionNet. The point-wise features were fused with their corresponding local features in the FF-Conv and then mapped into a high-dimensional space to extract richer local features. The GRRM inferred the relationship between various parts, in order to capture global features for enriching the content of the feature descriptor. Therefore the PointFusionNet is suitable for point clouds classification and semantic segmentation by using the two distinctive modules. The PointFusionNet has been tested on ModelNet40 and ShapeNet part datasets, and the experiments show that PointFusionNet has a competitive advantage in shape classification and part segmentation tasks.