Shape Reconstruction from 3D and 2D Data Using PDE-Based Deformable Surfaces

In this paper, we propose a new PDE-based methodology for deformable surfaces that is capable of automatically evolving its shape to capture the geometric boundary of the data and simultaneously discover its underlying topological structure. Our model can handle multiple types of data (such as volumetric data, 3D point clouds and 2D image data), using a common mathematical framework. The deformation behavior of the model is governed by partial differential equations (e.g. the weighted minimal surface flow). Unlike the level-set approach, our model always has an explicit representation of geometry and topology. The regularity of the model and the stability of the numerical integration process are ensured by a powerful Laplacian tangential smoothing operator. By allowing local adaptive refinement of the mesh, the model can accurately represent sharp features. We have applied our model for shape reconstruction from volumetric data, unorganized 3D point clouds and multiple view images. The...

• Type: Conference
• Year: 2004
• Where: ECCV
• Authors: Ye Duan, Liu Yang, Hong Qin, Dimitris Samaras
• Keywords: Computer Vision, Data Demonstrate Robustness, ECCV 2004, Laplacian Tangential Smoothing, Multiple View Reconstruction, Shape Reconstruction, Volumetric Data