| Summary: | Due to the irregularity and inconsistency of 3D point clouds, it is difficult to extract features directly from them. Existing methods usually extract point features independently and then use the max-pooling operation to aggregate local features, which limits the feature representation capability of their models. In this work, we design a novel spatial-related correlation path, which considers both spatial information and point correlations, to preserve high dimensional features, thereby capturing fine-detail information and long-distance context of the point cloud. We further propose a new network to aggregate the spatial aware correlations with point-wise features and global features in a learnable way. The experimental results show that our method can achieve better performance than the state-of-the-art approaches on challenging datasets. We can achieve 0.934 accuracy on ModelNet40 dataset and 0.875 mean IoU (Intersection over Union) on ShapeNet dataset with only about 2.42 million parameters.
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