  3D surface reconstruction from point clouds is a key step in areas such as
content creation, archaeology, digital cultural heritage, and engineering.
Current approaches either try to optimize a non-data-driven surface
representation to fit the points, or learn a data-driven prior over the
distribution of commonly occurring surfaces and how they correlate with
potentially noisy point clouds. Data-driven methods enable robust handling of
noise and typically either focus on a global or a local prior, which trade-off
between robustness to noise on the global end and surface detail preservation
on the local end. We propose PPSurf as a method that combines a global prior
based on point convolutions and a local prior based on processing local point
cloud patches. We show that this approach is robust to noise while recovering
surface details more accurately than the current state-of-the-art.
  Our source code, pre-trained model and dataset are available at:
https://github.com/cg-tuwien/ppsurf
