Arbitrary point cloud upsampling via Dual Back-Projection Network
Zhi-Song Liu Zijia Wang Zhen Jia
Point clouds acquired from 3D sensors are usually sparse and noisy. Point
cloud upsampling is an approach to increase the density of the point cloud so
that detailed geometric information can be restored. In this paper, we propose
a Dual Back-Projection network for point cloud upsampling (DBPnet). A Dual
Back-Projection is formulated in an up-down-up manner for point cloud
upsampling. It not only back projects feature residues but also coordinates
residues so that the network better captures the point correlations in the
feature and space domains, achieving lower reconstruction errors on both
uniform and non-uniform sparse point clouds. Our proposed method is also
generalizable for arbitrary upsampling tasks (e.g. 4x, 5.5x). Experimental
results show that the proposed method achieves the lowest point set matching
losses with respect to the benchmark. In addition, the success of our approach
demonstrates that generative networks are not necessarily needed for
non-uniform point clouds.