Learning Continuous Implicit Field with Local Distance Indicator for
  Arbitrary-Scale Point Cloud Upsampling
Shujuan Li Junsheng Zhou Baorui Ma Yu-Shen Liu Zhizhong Han
Point cloud upsampling aims to generate dense and uniformly distributed point
sets from a sparse point cloud, which plays a critical role in 3D computer
vision. Previous methods typically split a sparse point cloud into several
local patches, upsample patch points, and merge all upsampled patches. However,
these methods often produce holes, outliers or nonuniformity due to the
splitting and merging process which does not maintain consistency among local
patches. To address these issues, we propose a novel approach that learns an
unsigned distance field guided by local priors for point cloud upsampling.
Specifically, we train a local distance indicator (LDI) that predicts the
unsigned distance from a query point to a local implicit surface. Utilizing the
learned LDI, we learn an unsigned distance field to represent the sparse point
cloud with patch consistency. At inference time, we randomly sample queries
around the sparse point cloud, and project these query points onto the
zero-level set of the learned implicit field to generate a dense point cloud.
We justify that the implicit field is naturally continuous, which inherently
enables the application of arbitrary-scale upsampling without necessarily
retraining for various scales. We conduct comprehensive experiments on both
synthetic data and real scans, and report state-of-the-art results under widely
used benchmarks.