Models and algorithms for efficient multiresolution topology estimation of measured 3-D range data.

Area of Science:

• Computer Graphics
• Computational Geometry
• 3D Data Processing

Background:

• Constructing accurate polygonal meshes from 3D range data is crucial for various applications.
• Existing methods often face challenges in achieving desired mesh properties like regularity and equiangularity.
• Efficient topology estimation is key for processing large 3D datasets.

Purpose of the Study:

• To propose a novel and efficient algorithm for topology estimation in 3D range data.
• To construct multiresolution polygonal and triangular meshes with improved equiangularity.
• To address constraints of cognition, compactness, and regularity in mesh generation.

Main Methods:

• Segmentation of 3D range data into Voronoi patches using K-means clustering.
• Approximation of each patch with polygonal and triangular mesh models.
• Application of a dynamic mesh model to adaptively improve mesh equiangularity.

Main Results:

• Successful construction of satisfactory equiangular triangular mesh models.
• Rapid mesh generation at various resolutions.
• Tolerable modeling errors achieved.

Conclusions:

• The proposed algorithm efficiently generates high-quality multiresolution meshes from 3D data.
• The dynamic mesh model effectively enhances mesh equiangularity.
• The method is suitable for both point clouds and dense meshes, offering a robust solution for 3D modeling.