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:
this https URL

PPSurf 是一种结合了基于点卷积的全局先验和基于处理局部点云补丁的局部先验的方法，能够在恢复表面细节方面比当前最先进的方法更准确，同时对噪声具有鲁棒性。