Can I read PU-Net: Point Cloud Upsampling Network on EtoBox?

PU-Net: Point Cloud Upsampling Network by Yu, Lequan; Li, Xianzhi; Fu, Chi-Wing; Cohen-Or, Daniel; Heng, Pheng-Ann is a scholarly article available to read on EtoBox.

What is PU-Net: Point Cloud Upsampling Network about?

Learning and analyzing 3D point clouds with deep networks is challenging due to the sparseness and irregularity of the data. In this paper, we present a data-driven point cloud upsampling technique. The key idea is to learn multi-level features per point and expand the point set via a multi-branch convolution unit implicitly in feature space. The expanded feature is then split to a multitude of features, which are then reconstructed to an upsampled point set. Our network is applied at a patch-level, with a joint loss function that encourages the upsampled points to remain on the underlying surface with a uniform distribution. We conduct various experiments using synthesis and scan data to evaluate our method and demonstrate its superiority over some baseline methods and an optimization-based method. Results show that our upsampled points have better uniformity and are located closer to the underlying surfaces.

Author

Yu, Lequan; Li, Xianzhi; Fu, Chi-Wing; Cohen-Or, Daniel; Heng, Pheng-Ann

Published

2018

Language

EN