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

翻译：点云的三维表面重建是内容创作、考古学、数字文化遗产及工程等领域的关键步骤。现有方法要么试图优化非数据驱动的表面表示以拟合点云，要么学习数据驱动的先验知识以建模常见表面的分布及其与潜在噪声点云的关联。数据驱动方法能够鲁棒地处理噪声，通常侧重于全局先验或局部先验，二者在全局噪声鲁棒性与局部表面细节保留之间进行权衡。我们提出的PPSurf方法结合了基于点卷积的全局先验与基于局部点云补丁处理的局部先验。实验表明，该方法在保持对噪声鲁棒性的同时，能够比当前最先进方法更精确地重建表面细节。我们的源代码、预训练模型及数据集可通过以下链接获取：https://github.com/cg-tuwien/ppsurf