Frame points attention convolution for deep learning on point cloud

Point Cloud can be considered as non-Euclidean structure data since it is disordered and irregular. When training on point cloud, it is difficult to apply spatial discrete convolution directly. In this paper, we propose a novel three-dimensional spatial convolution operator called frame points attention convolution (FPAC). FPAC pre-defines a set of frame points in space and quantifies the correlation between the input local points and the frame points through an attention mechanism. FPAC then combines the quantified correlations with the weights of the frame points to generate spatially continuous filters. The convolution weights for different local areas in the filters are calculated dynamically, without relying on generative models or probabilistic assumptions. Furthermore, FPAC is reformulated to reduce the internal dimensions during training, which reduces memory consumption and significantly improves training speed. Several optimization measures are also implemented to further enhance the performance of FPAC. We built three common point cloud task networks using FPAC and conducted experiments to train these networks on widely used datasets. Experimental results show that the method proposed in this work is competitive with state-of-the-art methods for point cloud tasks.