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Deep learning on point clouds has made a lot of progress recently. Many pointcloud dedicated deep learning frameworks, such as PointNet and PointNet++, haveshown advantages in accuracy and speed comparing to those using traditional 3Dconvolution algorithms. However, nearly all of these methods face a challenge,since the coordinates of the point cloud are decided by the coordinate system,they cannot handle the problem of 3D transform invariance properly. In thispaper, we propose a general framework for point cloud learning. We achievetransform invariance by learning inner 3D geometry feature based on local graphrepresentation, and propose a feature extraction network based on graphconvolution network. Through experiments on classification and segmentationtasks, our method achieves state-of-the-art performance in rotated 3D objectclassification, and achieve competitive performance with the state-of-the-artin classification and segmentation tasks with fixed coordinate value.

3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN