Point Cloud Upsampling via a Coarse-to-Fine Network

Abstract

Point clouds captured by 3D scanning are usually sparse and noisy. Reconstructing a high-resolution 3D model of an object is a challenging task in computer vision. Recent point cloud upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface directly via an end-to-end network. Although dense reconstruction from low to high resolution can be realized by using these techniques, it lacks abundant details for dense outputs. In this work, we propose a coarse-to-fine network PUGL-Net for point cloud reconstruction that first predicts a coarse high-resolution point cloud via a global dense reconstruction module and then increases the details by aggregating local point features. On the one hand, a transformer-based mechanism is designed in the global dense reconstruction module. It aggregates residual learning in a self-attention scheme for effective global feature extraction. On the other hand, the coordinate offset of points is learned in a local refinement module. It further refines the coarse points by aggregating KNN features. Evaluated through extensive quantitative and qualitative evaluation on synthetic data set, the proposed coarse-to-fine architecture generates point clouds that are accurate, uniform and dense, it outperforms most existing state-of-the-art point cloud reconstruction works.