PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows
Published in IEEE Transactions on Visualization and Computer Graphics, 2022
Abstract: Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
Recommended citation: Aihua Mao, Zihui Du, Junhui Hou, Yaqi Duan, Yong-Jin Liu, Ying He. PU-Flow: a Point Cloud Upsampling Network with Normalizing Flows. IEEE Transactions on Visualization and Computer Graphics, 2022, doi: 10.1109/TVCG.2022.3196334.