Leveraging Graph Convolutional Networks for Point Cloud Upsampling

Abstract

Due to hardware limitations, 3D sensors like LiDAR often produce sparse and noisy point clouds. Point cloud upsampling is the task of converting such point clouds into dense and clean ones. This thesis tackles the problem of point cloud upsampling using deep neural networks. The effectiveness of a point cloud upsampling neural network heavily relies on the upsampling module and the feature extractor used therein. In this thesis, I propose a novel point upsampling module, called NodeShuffle. NodeShuffle leverages Graph Convolutional Networks (GCNs) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves the performance of previous upsampling methods. I also propose a new GCN-based multi-scale feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, Inception DenseGCN learns a hierarchical feature representation and enables further performance gains. I combine Inception DenseGCN with NodeShuffle into the proposed point cloud upsampling network called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.