.png){kind=icon}
.png){kind=icon}
Return
Computing refined skeletal features from medial point clouds
.png){kind=icon}
OA
AI
DOI：10.1016/j.patrec.2015.05.007.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Medial representations have been widely used for many shape analysis and processing tasks. Large and complex 3D shapes are, in this context, a challenging case. Recently, several methods have been proposed that extract point-based medial surfaces with high accuracy and computational scalability. However, the resulting medial clouds are of limited use for shape processing due to the difficulty of computing refined medial features from such clouds. In this paper, we show how to bridge the gap between having a raw medial cloud and enriching this cloud with feature points, medial-point classification, medial axis decomposition into sheets, robust regularization, and Y-network extraction. We further show how such properties can be used to support several shape processing sample applications including edge detection and shape segmentation, for a wide range of complex 3D shapes. (C) 2015 Elsevier B.V. All rights reserved.
Keywords:
Feature extraction
Shape segmentation
Point cloud
Skeleton segmentation
Polygonal
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
3.3
Papers:
7.8K
Citations:
1.6W