PPSurf: Combining Patches and Point Convolutions for Detailed Surface Reconstruction

Abstract

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 \emph{global} or a \emph{local} prior, which trade-off between robustness to noise on the global end and surface detail preservation on the local end. We propose \name 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.

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