SPU-IMR: Self-supervised Arbitrary-scale Point Cloud Upsampling via Iterative Mask-recovery Network
Journal:
arXiv
Published Date:
Feb 26, 2025
Abstract
Point cloud upsampling aims to generate dense and uniformly distributed point
sets from sparse point clouds. Existing point cloud upsampling methods
typically approach the task as an interpolation problem. They achieve
upsampling by performing local interpolation between point clouds or in the
feature space, then regressing the interpolated points to appropriate
positions. By contrast, our proposed method treats point cloud upsampling as a
global shape completion problem. Specifically, our method first divides the
point cloud into multiple patches. Then, a masking operation is applied to
remove some patches, leaving visible point cloud patches. Finally, our
custom-designed neural network iterative completes the missing sections of the
point cloud through the visible parts. During testing, by selecting different
mask sequences, we can restore various complete patches. A sufficiently dense
upsampled point cloud can be obtained by merging all the completed patches. We
demonstrate the superior performance of our method through both quantitative
and qualitative experiments, showing overall superiority against both existing
self-supervised and supervised methods.
