PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction

• URL: http://arxiv.org/abs/2401.08518v2
• Date: Thu, 8 Feb 2024 15:10:39 GMT
• Title: PPSURF: Combining Patches and Point Convolutions for Detailed Surface Reconstruction
• Authors: Philipp Erler and Lizeth Fuentes and Pedro Hermosilla and Paul Guerrero and Renato Pajarola and Michael Wimmer
• Abstract summary: PPSurf is 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.
• Score: 15.843730758485917
• License: http://creativecommons.org/licenses/by/4.0/
• 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 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

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