· 2017
PolyFit: Polygonal Surface Reconstruction from Point Clouds
- 281citations
- 2017year
Short summary
PolyFit reconstructs manifold polygonal surfaces from point clouds by intersecting planar primitives and selecting an optimal subset via binary linear programming, producing lightweight, watertight models that recover sharp features and are robust to noise.
AI-generated from the title and abstract; the full text is not read.
Key points
- Reconstructs polygonal surfaces from point clouds by intersecting planar primitives.
- Uses binary linear programming to select an optimal subset of candidate faces.
- Enforces manifold and watertight properties through hard constraints.
- Generates lightweight models capable of recovering sharp features.
- Demonstrates robustness to noise, outliers, and missing data.
AI-generated from the title and abstract; the full text is not read.
Abstract
We propose a novel framework for reconstructing lightweight polygonal surfaces from point clouds. Unlike traditional methods that focus on either extracting good geometric primitives or obtaining proper arrangements of primitives, the emphasis of this work lies in intersecting the primitives (planes only) and seeking for an appropriate combination of them to obtain a manifold polygonal surface model without boundary. We show that reconstruction from point clouds can be cast as a binary labeling problem. Our method is based on a hypothesizing and selection strategy. We first generate a reasonably large set of face candidates by intersecting the extracted planar primitives. Then an optimal subset of the candidate faces is selected through optimization. Our optimization is based on a binary linear programming formulation under hard constraints that enforce the final polygonal surface model to be manifold and watertight. Experiments on point clouds from various sources demonstrate that our method can generate lightweight polygonal surface models of arbitrary piecewise planar objects. Besides, our method is capable of recovering sharp features and is robust to noise, outliers, and missing data.
The authors' abstract, as published at the source., 2017 · DOI ↗
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Field: Computer Graphics and Computer-Aided Design
Computer Graphics and Computer-Aided DesignComputer Science