Iterative Poisson Surface Reconstruction (iPSR) for Unoriented Points

Abstract

Poisson surface reconstruction (PSR) remains a popular technique for reconstructing watertight surfaces from 3D point samples thanks to its efficiency, simplicity, and robustness. Yet, the existing PSR method and subsequent variants work only for oriented points. This paper intends to validate that an improved PSR, called iPSR, can completely eliminate the requirement of point normals and proceed in an iterative manner. In each iteration, iPSR takes as input point samples with normals directly computed from the surface obtained in the preceding iteration, and then generates a new surface with better quality. Extensive quantitative evaluation confirms that the new iPSR algorithm converges in 5-30 iterations even with randomly initialized normals. If initialized with a simple visibility based heuristic, iPSR can further reduce the number of iterations. We conduct comprehensive comparisons with PSR and other powerful implicit-function based methods. Finally, we confirm iPSR's effectiveness and scalability on the AIM@SHAPE dataset and challenging (indoor and outdoor) scenes. Code and data for this paper are at https://github.com/houfei0801/ipsr.

References in corpus (5)

• Point2Mesh: A Self-Prior for Deformable Meshes
• Points2Surf: Learning Implicit Surfaces from Point Cloud Patches
• Neural Marching Cubes
• Orienting Point Clouds with Dipole Propagation
• Implicit Geometric Regularization for Learning Shapes

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• LFS-Aware Surface Reconstruction from Unoriented 3D Point Clouds
• Learning Self-Prior for Mesh Inpainting Using Self-Supervised Graph Convolutional Networks
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• Winding Clearness for Differentiable Point Cloud Optimization
• FlatCAD: Fast Curvature Regularization of Neural SDFs for CAD Models
• A Geometric Algorithm for Blood Vessel Reconstruction from Skeletal Representation