Deep learning on point clouds has made a lot of progress recently. Many point cloud dedicated deep learning frameworks, such as PointNet and PointNet++, have shown advantages in accuracy and speed comparing to those using traditional 3D convolution 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 this paper, we propose a general framework for point cloud learning. We achieve transform invariance by learning inner 3D geometry feature based on local graph representation, and propose a feature extraction network based on graph convolution network. Through experiments on classification and segmentation tasks, our method achieves state-of-the-art performance in rotated 3D object classification, and achieve competitive performance with the state-of-the-art in classification and segmentation tasks with fixed coordinate value.
@article{arxiv.1812.06254,
title = {3DTI-Net: Learn Inner Transform Invariant 3D Geometry Features using Dynamic GCN},
author = {Guanghua Pan and Jun Wang and Rendong Ying and Peilin Liu},
journal= {arXiv preprint arXiv:1812.06254},
year = {2018}
}