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A subdivision-based framework for shape reconstruction
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AI
DOI：10.1007/s11042-023-15398-7.png)
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Abstract
En 中文
Shape reconstruction from 3D point clouds is one of the most important topic in the field of computer graphics. In this paper, we propose a subdivision-based framework for this topic. The framework includes two parts: distance field optimization and mesh generation. The first part optimizes a point cloud into an approximately isotropic one based on a subdivision structure. The second part is to generate a triangular mesh from the optimized point cloud. The mesh is regarded as the result of shape reconstruction. The advantages of our method includes accurate geometric consistency, improved mesh quality, controllable point number, and fast speed. Experiments indicate that our method has good performance for shape reconstruction (compare to the state-of-the-art, our method achieves five and six times improvement in Hausdorff distance-based measurement and density estimation). The executable file is available: (https://github.com/vvvwo/Parallel-Structure-ShapeReconstruction)
Keywords:
Shape reconstruction
Mesh reconstruction
Distance field optimisation
Journal
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3
Papers:
1.9W
Citations:
3.2W