Analysis of Global Properties of Shapes

Abstract

With increasing amounts of data describing 3D geometry at scales small and large, shape analysis is becoming increasingly important in fields ranging from computer graphics to robotics to computational biology. While a great deal of research exists on local shape analysis, less work has been done on global shape analysis. This thesis aims to advance global shape analysis in three directions: symmetry-aware mesh processing, part decomposition of 3D models, and analysis of 3D scenes. First, we propose a pipeline for making mesh processing algorithms "symmetry-aware," using large-scale symmetries to aid the processing of 3D meshes. Our pipeline can be used to emphasize the symmetries of a mesh, establish correspondences between symmetric features of a mesh, and decompose a mesh into symmetric parts and asymmetric residuals. We make technical contributions towards two of the main steps in this pipeline: a method for symmetrizing the geometry of an object, and a method for remeshing an object to have a symmetric triangulation. We offer several applications of this pipeline: modeling, beautification, attribute transfer, and simplification of approximately symmetric surfaces. Second, we conduct several investigations into part decomposition of 3D meshes. We propose a hierarchical mesh segmentation method as a basis for consistently segmenting a set of meshes. We show how our method of consistent segmentation can be used for the more specific applications of symmetric segmentation and segmentation transfer. Then, we propose a probabilistic version of mesh segmentation, which we call a "partition function,"that aims to estimate the likelihood that a given mesh edge is on a segmentation boundary. We describe several methods of computing this structure, and demonstrate its robustness to noise, tessellation, and pose and intra-class shape variation. We demonstrate the utility of the partition function for mesh visualization, segmentation deformation, and registration.

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Document Details

Document Type
Technical Report
Publication Date
Jun 01, 2010
Accession Number
ADA571387

Entities

People

  • Aleksey Golovinskiy

Organizations

  • Princeton University

Tags

Communities of Interest

  • Air Platforms
  • Biomedical
  • Energy and Power Technologies

DTIC Thesaurus Topics

  • Algorithms
  • Computational Science
  • Computer Graphics
  • Computer Science
  • Computer Vision
  • Detection
  • Feature Extraction
  • Geometric Processing
  • Geometry
  • Graphics
  • Image Processing
  • Machine Learning
  • Object Recognition
  • Point Clouds
  • Recognition
  • Supervised Machine Learning
  • Two Dimensional

Fields of Study

  • Computer science

Readers

  • Computational Fluid Dynamics (CFD)
  • Computer Vision.
  • Graph Algorithms and Convex Optimization.

Technology Areas

  • AI & ML
  • Autonomy