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.

3D 传感器获取的点云通常稀疏且嘈杂，点云上采样是一种提高点云密度以恢复详细几何信息的方法。本文提出了一种用于点云上采样的双重背投影网络（DBPnet）。通过采用上下上的方式进行双重背投影，网络不仅对特征残差进行反投影，还对坐标残差进行反投影，从而更好地捕捉特征和空间域中的点关联性，并在均匀和非均匀稀疏点云上实现较低的重构误差。我们的方法也适用于任意上采样任务（例如 4 倍，5.5 倍）。实验结果表明，该方法在与基准点集匹配损失方面实现了最低值。此外，我们方法的成功也证明了非均匀点云不一定需要生成网络。