Abstract
With the rapid development of autonomous driving, point cloud is becoming a hot research hotspot Although the current network has solved the irregular structures and sparsity of point clouds, the geometric transformation of point clouds remains challenging problems. We propose a new graph convolution method, which has the ability to learn the geometric invariance features of point clouds. We define the convolution kernel as a graph, and add the maximum angle feature between each edge of the convolution kernel and the receptive field graph. We propose a new graph pyramid pooling, which integrates multi-scale features to learn local features better. We evaluate our method on multiple point cloud analysis tasks, including shape classification and part segmentation. Experimental results show that our method presents the state-of-the-art performance on the rotation-augmented object classification and segmentation.